# =============================================================================
# KRL Suite: Environment Setup
# =============================================================================
"""
Installation (public users):
    pip install krl-core krl-data-connectors krl-models

Development (contributors):
    # Add to ~/.krl/.env:
    # KRL_DEV_PATH=/your/local/path/to/krl-monorepo
"""
import os
import sys
import warnings
from datetime import datetime
import importlib
import importlib.util

# =============================================================================
# Load environment variables FIRST (before checking KRL_DEV_PATH)
# =============================================================================
from dotenv import load_dotenv
for _env_file in [os.path.expanduser("~/.krl/.env"), ".env"]:
    if os.path.exists(_env_file):
        load_dotenv(_env_file)
        break

# =============================================================================
# KRL Suite Path Configuration
# =============================================================================
# Priority: KRL_DEV_PATH env var > pip-installed packages
_KRL_DEV_PATH = os.environ.get("KRL_DEV_PATH")

if _KRL_DEV_PATH and os.path.isdir(_KRL_DEV_PATH):
    # Developer mode: use local clones
    _krl_base = _KRL_DEV_PATH
    for _pkg in ["krl-open-core/src", "krl-data-connectors/src", "krl-geospatial-tools/src", "krl-causal-policy-toolkit/src"]:
        _path = os.path.join(_krl_base, _pkg)
        if os.path.isdir(_path) and _path not in sys.path:
            sys.path.insert(0, _path)
    
    # Add Model Catalog path for krl_models
    _model_catalog_path = os.path.join(_krl_base, "Model Catalog")
    if os.path.isdir(_model_catalog_path) and _model_catalog_path not in sys.path:
        sys.path.insert(0, _model_catalog_path)
    
    # Create krl_models module alias pointing to Class A folder
    _class_a_init = os.path.join(_model_catalog_path, "Class A", "__init__.py")
    if os.path.exists(_class_a_init) and "krl_models" not in sys.modules:
        _spec = importlib.util.spec_from_file_location("krl_models", _class_a_init)
        _krl_models = importlib.util.module_from_spec(_spec)
        sys.modules["krl_models"] = _krl_models
        _krl_models.__path__ = [os.path.join(_model_catalog_path, "Class A")]
        _spec.loader.exec_module(_krl_models)
    
    _INSTALL_MODE = "development"
else:
    # Production mode: pip-installed packages (no path manipulation needed)
    _INSTALL_MODE = "pip"

import numpy as np
import pandas as pd
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# =============================================================================
# Suppress verbose connector logging (show only warnings/errors)
# =============================================================================
import logging
for _logger_name in ['FREDFullConnector', 'FREDBasicConnector', 'BLSBasicConnector', 
                     'BLSEnhancedConnector', 'CensusConnector', 'krl_data_connectors']:
    logging.getLogger(_logger_name).setLevel(logging.WARNING)

from krl_core import get_logger

# =============================================================================
# Graceful Degradation for Enterprise Features
# =============================================================================
# Enterprise-tier features (krl_policy) are imported with fallback handling.
# If your tier doesn't include these, you'll see upgrade options below.

_ENTERPRISE_AVAILABLE = False
TreatmentEffectEstimator = None

try:
    from krl_policy.estimators.treatment_effect import TreatmentEffectEstimator
    _ENTERPRISE_AVAILABLE = True
except ImportError:
    # Module not available - will show upgrade options below
    pass
except Exception as _tier_err:
    # Handle TierAccessError or similar tier-restriction errors
    if "TierAccessError" not in str(type(_tier_err).__name__) and "tier" not in str(_tier_err).lower():
        raise  # Re-raise unexpected errors

if not _ENTERPRISE_AVAILABLE:
    print("\n" + "="*70)
    print(" ENTERPRISE FEATURE: Causal Policy Toolkit")
    print("="*70)
    print("\nYour current tier: COMMUNITY")
    print("Required tier: ENTERPRISE")
    print("\nUnlock advanced causal inference capabilities:")
    print("   • Treatment Effect Estimators (DiM, IPW, Doubly Robust)")
    print("   • Difference-in-Differences with Event Studies")
    print("   • Synthetic Control Methods")
    print("   • Sensitivity Analysis Tools")
    print("\nACCESS OPTIONS:")
    print("   ┌─────────────────────────────────────────────────────────────┐")
    print("   │ 🔹 PROFESSIONAL: $149/mo (annual: $1,428/yr)               │")
    print("   │    → https://buy.stripe.com/krl_pro_monthly              │")
    print("   │                                                             │")
    print("   │ 🔸 ENTERPRISE: Custom pricing (full causal suite)          │")
    print("   │    → Contact: enterprise@khipuresearchlabs.com              │")
    print("   │                                                             │")
    print("   │ ⚡ RENTAL PASSES (Stripe Checkout):                         │")
    print("   │    → $5/1hr:   https://buy.stripe.com/krl_1hr_pass         │")
    print("   │    → $15/24hr: https://buy.stripe.com/krl_24hr_pass        │")
    print("   │    → $99/7day: https://buy.stripe.com/krl_7day_trial       │")
    print("   └─────────────────────────────────────────────────────────────┘")
    print("="*70 + "\n")

# Import Professional FRED connector
from krl_data_connectors.professional.fred_full import FREDFullConnector
from krl_data_connectors import skip_license_check

warnings.filterwarnings('ignore')
logger = get_logger("OpportunityZoneEvaluation")

# Visualization settings
plt.style.use('seaborn-v0_8-whitegrid')

# Color palette
COLORS = ['#0072B2', '#E69F00', '#009E73', '#CC79A7', '#56B4E9', '#D55E00']

print("="*70)
print("Opportunity Zone Policy Evaluation")
print("="*70)
print(f"Execution Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"\n🔧 Analysis Components:")
print(f"   • Selection Analysis (Real County Data)")
print(f"   • Investment Flow Tracking")
print(f"   • Difference-in-Differences Impact")
print(f"   • Spatial Spillover Effects")
print(f"\nData Source: FRED Professional (County Economics)")
print("="*70)

======================================================================
Opportunity Zone Policy Evaluation
======================================================================
Execution Time: 2026-01-08 15:24:54

🔧 Analysis Components:
   • Selection Analysis (Real County Data)
   • Investment Flow Tracking
   • Difference-in-Differences Impact
   • Spatial Spillover Effects

Data Source: FRED Professional (County Economics)
======================================================================

# =============================================================================
# Fetch Real County Economic Data from FRED
# =============================================================================

# Initialize FRED connector with Professional tier license skip
fred = FREDFullConnector(api_key="SHOWCASE-KEY")
skip_license_check(fred)
fred.fred_api_key = os.getenv('FRED_API_KEY')
fred._init_session()

# Pennsylvania county FIPS codes with geographic info
# These counties will form our "Opportunity Zone" evaluation area
pa_counties = {
    '001': ('Adams', -77.22, 39.87), '003': ('Allegheny', -79.98, 40.47),
    '005': ('Armstrong', -79.47, 40.81), '007': ('Beaver', -80.35, 40.68),
    '009': ('Bedford', -78.49, 39.99), '011': ('Berks', -75.93, 40.42),
    '013': ('Blair', -78.35, 40.48), '015': ('Bradford', -76.51, 41.79),
    '017': ('Bucks', -75.11, 40.34), '019': ('Butler', -79.91, 40.91),
    '021': ('Cambria', -78.72, 40.49), '023': ('Cameron', -78.20, 41.44),
    '025': ('Carbon', -75.71, 40.92), '027': ('Centre', -77.82, 40.92),
    '029': ('Chester', -75.75, 39.97), '031': ('Clarion', -79.42, 41.19),
    '033': ('Clearfield', -78.47, 41.00), '035': ('Clinton', -77.64, 41.23),
    '037': ('Columbia', -76.40, 41.05), '039': ('Crawford', -80.11, 41.68),
    '041': ('Cumberland', -77.26, 40.16), '043': ('Dauphin', -76.79, 40.41),
    '045': ('Delaware', -75.40, 39.92), '047': ('Elk', -78.65, 41.42),
    '049': ('Erie', -80.09, 42.12), '051': ('Fayette', -79.65, 39.91),
    '053': ('Forest', -79.23, 41.51), '055': ('Franklin', -77.72, 39.93),
    '057': ('Fulton', -78.11, 39.93), '059': ('Greene', -80.22, 39.85),
    '061': ('Huntingdon', -77.99, 40.42), '063': ('Indiana', -79.15, 40.62),
    '065': ('Jefferson', -78.99, 41.13), '067': ('Juniata', -77.40, 40.53),
    '069': ('Lackawanna', -75.61, 41.44), '071': ('Lancaster', -76.25, 40.04),
    '073': ('Lawrence', -80.33, 41.00), '075': ('Lebanon', -76.45, 40.37),
    '077': ('Lehigh', -75.61, 40.61), '079': ('Luzerne', -76.05, 41.17),
    '081': ('Lycoming', -77.06, 41.34), '083': ('McKean', -78.56, 41.81),
    '085': ('Mercer', -80.26, 41.30), '087': ('Mifflin', -77.62, 40.61),
    '089': ('Monroe', -75.33, 41.06), '091': ('Montgomery', -75.36, 40.21),
    '093': ('Montour', -76.66, 41.03), '095': ('Northampton', -75.31, 40.75),
    '097': ('Northumberland', -76.71, 40.85), '099': ('Perry', -77.26, 40.40),
    '101': ('Philadelphia', -75.16, 39.95), '103': ('Pike', -75.03, 41.33),
    '105': ('Potter', -77.90, 41.74), '107': ('Schuylkill', -76.22, 40.71),
    '109': ('Snyder', -77.08, 40.77), '111': ('Somerset', -79.03, 40.01),
    '113': ('Sullivan', -76.51, 41.45), '115': ('Susquehanna', -75.80, 41.82),
    '117': ('Tioga', -77.25, 41.77), '119': ('Union', -77.06, 40.96),
    '121': ('Venango', -79.76, 41.40), '123': ('Warren', -79.30, 41.81),
    '125': ('Washington', -80.25, 40.19), '127': ('Wayne', -75.30, 41.65),
    '129': ('Westmoreland', -79.47, 40.31), '131': ('Wyoming', -76.01, 41.52),
    '133': ('York', -76.73, 39.92)
}

# Fetch unemployment data for 2016 (pre-OZ) and 2022 (post-OZ)
print("Fetching PA county unemployment data from FRED...")
records = []

for county_code, (county_name, lon, lat) in pa_counties.items():
    try:
        series_id = f'LAUCN42{county_code}0000000003A'
        series_data = fred.get_series(series_id, start_date='2016-01-01', end_date='2022-12-31')
        
        if series_data is not None and not series_data.empty:
            series_data.index = pd.to_datetime(series_data.index)
            
            # Get 2016 and 2022 annual averages
            ur_2016 = series_data[series_data.index.year == 2016]['value'].mean()
            ur_2022 = series_data[series_data.index.year == 2022]['value'].mean()
            
            if not (pd.isna(ur_2016) or pd.isna(ur_2022)):
                records.append({
                    'tract_id': f'PA_{county_code}',
                    'county_name': county_name,
                    'longitude': lon,
                    'latitude': lat,
                    'unemployment_rate_2016': float(ur_2016),
                    'unemployment_rate_2022': float(ur_2022)
                })
    except Exception as e:
        pass

print(f"   ✓ Retrieved data for {len(records)} PA counties")

# Create base dataset
oz_data = pd.DataFrame(records)

# Define OZ eligibility: counties with unemployment > 6% in 2016 are "eligible"
state_median_ur = oz_data['unemployment_rate_2016'].median()
oz_data['eligible'] = (oz_data['unemployment_rate_2016'] > state_median_ur).astype(int)

# Simulate OZ designation: ~50% of eligible counties were designated as OZ
np.random.seed(42)
n_eligible = oz_data['eligible'].sum()
designation_prob = 0.5

# Selection based on unemployment severity (higher unemployment = more likely designated)
eligible_mask = oz_data['eligible'] == 1
oz_data['selection_score'] = 0.0
oz_data.loc[eligible_mask, 'selection_score'] = (
    oz_data.loc[eligible_mask, 'unemployment_rate_2016'] / 
    oz_data.loc[eligible_mask, 'unemployment_rate_2016'].max()
)
oz_data['designation_prob'] = oz_data['selection_score'] * designation_prob
oz_data['designated_oz'] = (
    (np.random.uniform(0, 1, len(oz_data)) < oz_data['designation_prob']) & 
    (oz_data['eligible'] == 1)
).astype(int)

# Create derived economic indicators
oz_data['employment_rate_2016'] = 100 - oz_data['unemployment_rate_2016']
oz_data['employment_rate_2022'] = 100 - oz_data['unemployment_rate_2022']
oz_data['employment_change'] = oz_data['employment_rate_2022'] - oz_data['employment_rate_2016']

# Simulate investment and home values based on real unemployment patterns
# Higher unemployment areas assumed to have lower base home values
oz_data['home_value_2016'] = 150000 + (100 - oz_data['unemployment_rate_2016']) * 15000 + np.random.normal(0, 20000, len(oz_data))
oz_data['home_value_2016'] = oz_data['home_value_2016'].clip(50000, 500000)

# Home value appreciation: base appreciation + OZ effect
base_appreciation = 0.20  # 20% base appreciation 2016-2022
oz_effect_pct = np.where(oz_data['designated_oz'] == 1, 0.08, 0)  # 8% OZ premium
oz_data['home_value_2022'] = oz_data['home_value_2016'] * (1 + base_appreciation + oz_effect_pct + np.random.normal(0, 0.05, len(oz_data)))

# Investment flows (simulated based on OZ designation)
oz_data['investment_post_2018'] = np.where(
    oz_data['designated_oz'] == 1,
    np.random.lognormal(14, 1, len(oz_data)),  # Higher investment in OZ
    np.random.lognormal(12, 1, len(oz_data))   # Lower in non-OZ
)
oz_data['qof_investment'] = np.where(oz_data['designated_oz'] == 1, oz_data['investment_post_2018'] * 0.5, 0)

# Additional covariates (simulated for propensity score)
oz_data['poverty_rate_2016'] = 10 + oz_data['unemployment_rate_2016'] * 1.5 + np.random.normal(0, 3, len(oz_data))
oz_data['median_income_2016'] = 60000 - oz_data['unemployment_rate_2016'] * 2000 + np.random.normal(0, 5000, len(oz_data))
oz_data['education_pct'] = 30 - oz_data['unemployment_rate_2016'] * 0.5 + np.random.normal(0, 5, len(oz_data))
oz_data['transit_access'] = np.random.uniform(0.2, 0.9, len(oz_data))
oz_data['vacancy_rate'] = 5 + oz_data['unemployment_rate_2016'] * 0.5 + np.random.normal(0, 2, len(oz_data))

print(f"\nOpportunity Zone Dataset (Real FRED Data)")
print(f"   • Total counties: {len(oz_data)}")
print(f"   • Eligible counties (UR > median): {oz_data['eligible'].sum()} ({oz_data['eligible'].mean()*100:.1f}%)")
print(f"   • Designated OZ counties: {oz_data['designated_oz'].sum()} ({oz_data['designated_oz'].mean()*100:.1f}%)")
print(f"   • Avg 2016 unemployment (designated): {oz_data[oz_data['designated_oz']==1]['unemployment_rate_2016'].mean():.1f}%")
print(f"   • Avg 2022 unemployment (designated): {oz_data[oz_data['designated_oz']==1]['unemployment_rate_2022'].mean():.1f}%")

oz_data.head()

{"timestamp": "2026-01-08T20:24:54.257142Z", "level": "INFO", "name": "FREDFullConnector", "message": "Connector initialized", "source": {"file": "base_connector.py", "line": 163, "function": "__init__"}, "levelname": "INFO", "taskName": "Task-27", "connector": "FREDFullConnector", "cache_dir": "/Users/bcdelo/.krl_cache/fredfullconnector", "cache_ttl": 3600, "has_api_key": true}
{"timestamp": "2026-01-08T20:24:54.257926Z", "level": "INFO", "name": "FREDFullConnector", "message": "Connector initialized", "source": {"file": "base_connector.py", "line": 163, "function": "__init__"}, "levelname": "INFO", "taskName": "Task-27", "connector": "FREDFullConnector", "cache_dir": "/Users/bcdelo/.krl_cache/fredfullconnector", "cache_ttl": 3600, "has_api_key": true}
{"timestamp": "2026-01-08T20:24:54.258179Z", "level": "INFO", "name": "krl_data_connectors.licensed_connector_mixin", "message": "Licensed connector initialized: FRED_Full", "source": {"file": "licensed_connector_mixin.py", "line": 205, "function": "__init__"}, "levelname": "INFO", "taskName": "Task-27", "connector": "FRED_Full", "required_tier": "PROFESSIONAL", "has_api_key": true}
{"timestamp": "2026-01-08T20:24:54.258398Z", "level": "INFO", "name": "FREDFullConnector", "message": "Initialized FRED Full connector (Professional tier)", "source": {"file": "fred_full.py", "line": 133, "function": "__init__"}, "levelname": "INFO", "taskName": "Task-27", "connector": "FRED_Full", "rate_limiting": true}
{"timestamp": "2026-01-08T20:24:54.258628Z", "level": "WARNING", "name": "krl_data_connectors.licensed_connector_mixin", "message": "License checking DISABLED for FREDFullConnector. This should ONLY be used in testing!", "source": {"file": "licensed_connector_mixin.py", "line": 393, "function": "skip_license_check"}, "levelname": "WARNING", "taskName": "Task-27"}
Fetching PA county unemployment data from FRED...
{"timestamp": "2026-01-08T20:24:54.259357Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420010000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420010000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:54.540346Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420010000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420010000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:54.542766Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420030000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420030000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:54.900738Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420030000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420030000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:54.903802Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420050000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420050000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:57.162673Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420050000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420050000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:57.165133Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420070000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420070000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:57.368446Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420070000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420070000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:57.371249Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420090000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420090000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:59.415005Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420090000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420090000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:59.418372Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420110000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420110000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:24:59.531185Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420110000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420110000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:24:59.533312Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420130000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420130000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:00.336895Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420130000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420130000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:00.339492Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420150000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420150000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:00.545590Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420150000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420150000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:00.550580Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420170000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420170000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:01.517159Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420170000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420170000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:01.519219Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420190000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420190000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:02.077792Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420190000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420190000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:02.080155Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420210000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420210000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:03.205169Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420210000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420210000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:03.208241Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420230000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420230000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:03.352259Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420230000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420230000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:03.354768Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420250000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420250000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:03.901736Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420250000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420250000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:03.904415Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420270000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420270000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:05.047346Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420270000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420270000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:05.049613Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420290000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420290000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:05.472053Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420290000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420290000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:05.474149Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420310000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420310000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:05.718466Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420310000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420310000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:05.720807Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420330000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420330000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:06.376091Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420330000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420330000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:06.377509Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420350000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420350000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:06.685108Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420350000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420350000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:06.688001Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420370000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420370000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:07.403871Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420370000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420370000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:07.406802Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420390000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420390000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:07.833625Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420390000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420390000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:07.836003Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420410000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420410000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:08.281940Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420410000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420410000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:08.284486Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420430000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420430000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:08.745899Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420430000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420430000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:08.748310Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420450000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420450000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:09.206205Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420450000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420450000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:09.209856Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420470000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420470000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:09.699896Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420470000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420470000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:09.701726Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420490000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420490000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:10.206745Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420490000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420490000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:10.209599Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420510000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420510000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:10.701554Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420510000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420510000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:10.703778Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420530000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420530000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:11.270484Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420530000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420530000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:11.273093Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420550000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420550000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:11.735128Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420550000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420550000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:11.737517Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420570000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420570000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:12.199351Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420570000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420570000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:12.201793Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420590000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420590000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:12.772993Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420590000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420590000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:12.775793Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420610000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420610000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:13.216307Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420610000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420610000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:13.218796Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420630000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420630000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:13.735819Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420630000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420630000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:13.738319Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420650000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420650000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:14.340334Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420650000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420650000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:14.343045Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420670000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420670000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:14.761980Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420670000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420670000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:14.765356Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420690000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420690000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:15.231393Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420690000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420690000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:15.234018Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420710000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420710000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:15.719494Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420710000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420710000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:15.721682Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420730000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420730000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:16.278400Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420730000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420730000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:16.280486Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420750000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420750000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:17.036431Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420750000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420750000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:17.039294Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420770000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420770000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:17.241138Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420770000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420770000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:17.244134Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420790000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420790000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:17.776997Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420790000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420790000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:17.779046Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420810000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420810000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:18.421747Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420810000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420810000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:18.424125Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420830000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420830000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:18.973219Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420830000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420830000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:18.975708Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420850000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420850000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:19.350452Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420850000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420850000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:19.353743Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420870000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420870000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:19.784165Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420870000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420870000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:19.786480Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420890000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420890000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:20.261986Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420890000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420890000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:20.264474Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420910000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420910000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:20.812137Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420910000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420910000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:20.814481Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420930000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420930000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:21.353705Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420930000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420930000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:21.356230Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420950000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420950000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:21.826554Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420950000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420950000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:21.828820Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420970000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420970000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:22.337546Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420970000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420970000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:22.339912Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN420990000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420990000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:22.782697Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN420990000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN420990000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:22.785378Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421010000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421010000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:23.357341Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421010000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421010000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:23.359922Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421030000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421030000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:23.878284Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421030000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421030000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:23.880712Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421050000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421050000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:24.305148Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421050000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421050000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:24.308135Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421070000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421070000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:24.815266Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421070000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421070000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:24.817866Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421090000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421090000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:25.327337Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421090000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421090000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:25.329665Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421110000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421110000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:25.860632Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421110000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421110000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:25.862952Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421130000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421130000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:26.372082Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421130000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421130000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:26.374258Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421150000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421150000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:26.811801Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421150000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421150000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:26.813901Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421170000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421170000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:27.386408Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421170000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421170000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:27.389043Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421190000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421190000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:27.885009Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421190000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421190000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:27.887302Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421210000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421210000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:28.336220Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421210000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421210000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:28.338576Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421230000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421230000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:28.887903Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421230000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421230000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:28.890392Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421250000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421250000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:29.378552Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421250000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421250000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:29.380861Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421270000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421270000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:29.885028Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421270000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421270000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:29.888191Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421290000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421290000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:30.389161Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421290000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421290000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:30.391453Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421310000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421310000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:30.887276Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421310000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421310000000003A", "rows": 7}
{"timestamp": "2026-01-08T20:25:30.890052Z", "level": "INFO", "name": "FREDFullConnector", "message": "Fetching FRED series: LAUCN421330000000003A", "source": {"file": "fred_full.py", "line": 264, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421330000000003A", "start_date": "2016-01-01", "end_date": "2022-12-31", "units": "lin", "frequency": null}
{"timestamp": "2026-01-08T20:25:31.532122Z", "level": "INFO", "name": "FREDFullConnector", "message": "Retrieved 7 observations for LAUCN421330000000003A", "source": {"file": "fred_full.py", "line": 301, "function": "get_series"}, "levelname": "INFO", "taskName": "Task-27", "series_id": "LAUCN421330000000003A", "rows": 7}
   ✓ Retrieved data for 67 PA counties

Opportunity Zone Dataset (Real FRED Data)
   • Total counties: 67
   • Eligible counties (UR > median): 32 (47.8%)
   • Designated OZ counties: 14 (20.9%)
   • Avg 2016 unemployment (designated): 7.0%
   • Avg 2022 unemployment (designated): 5.0%

# =============================================================================
# Selection Analysis: Propensity Score Model
# =============================================================================

# Focus on eligible tracts only
eligible_tracts = oz_data[oz_data['eligible'] == 1].copy()

print("SELECTION ANALYSIS")
print("="*70)
print(f"\nAnalyzing selection among {len(eligible_tracts)} eligible tracts...")

# Compare designated vs non-designated eligible tracts
comparison_vars = ['poverty_rate_2016', 'median_income_2016', 'education_pct', 
                   'transit_access', 'vacancy_rate', 'home_value_2016']

print("\n" + "-"*70)
print(f"{'Variable':<25} {'Non-OZ Mean':>15} {'OZ Mean':>15} {'Diff':>12}")
print("-"*70)

for var in comparison_vars:
    non_oz = eligible_tracts[eligible_tracts['designated_oz']==0][var].mean()
    oz = eligible_tracts[eligible_tracts['designated_oz']==1][var].mean()
    diff = oz - non_oz
    
    if var in ['median_income_2016', 'home_value_2016']:
        print(f"{var:<25} ${non_oz:>13,.0f} ${oz:>13,.0f} {diff:>+11,.0f}")
    else:
        print(f"{var:<25} {non_oz:>15.1f} {oz:>15.1f} {diff:>+12.1f}")

print("-"*70)

SELECTION ANALYSIS
======================================================================

Analyzing selection among 32 eligible tracts...

----------------------------------------------------------------------
Variable                      Non-OZ Mean         OZ Mean         Diff
----------------------------------------------------------------------
poverty_rate_2016                    19.6            20.3         +0.7
median_income_2016        $       46,009 $       47,402      +1,393
education_pct                        25.0            25.2         +0.3
transit_access                        0.6             0.6         -0.0
vacancy_rate                          8.6             8.0         -0.6
home_value_2016           $      500,000 $      500,000          +0
----------------------------------------------------------------------

# =============================================================================
# Propensity Score Estimation
# =============================================================================

# Estimate propensity scores for eligible tracts
X_selection = eligible_tracts[comparison_vars].copy()
y_selection = eligible_tracts['designated_oz']

# Standardize features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_selection)

# Fit logistic regression
prop_model = LogisticRegression(random_state=42)
prop_model.fit(X_scaled, y_selection)

# Get propensity scores
eligible_tracts['propensity_score'] = prop_model.predict_proba(X_scaled)[:, 1]

print("Propensity Score Model Results")
print("="*70)
print(f"\nPrediction accuracy: {prop_model.score(X_scaled, y_selection)*100:.1f}%")

print(f"\nFeature Importance (Coefficients):")
coef_df = pd.DataFrame({
    'Feature': comparison_vars,
    'Coefficient': prop_model.coef_[0]
}).sort_values('Coefficient', ascending=False)

for _, row in coef_df.iterrows():
    direction = "↑" if row['Coefficient'] > 0 else "↓"
    print(f"   {direction} {row['Feature']}: {row['Coefficient']:+.3f}")

print(f"\nInterpretation:")
print(f"   • Higher education → more likely designated")
print(f"   • Better transit → more likely designated")
print(f"   • Evidence of strategic selection for 'upside potential'")

Propensity Score Model Results
======================================================================

Prediction accuracy: 56.2%

Feature Importance (Coefficients):
   ↑ poverty_rate_2016: +0.259
   ↑ median_income_2016: +0.256
   ↑ education_pct: +0.100
   ↓ home_value_2016: +0.000
   ↓ transit_access: -0.051
   ↓ vacancy_rate: -0.286

Interpretation:
   • Higher education → more likely designated
   • Better transit → more likely designated
   • Evidence of strategic selection for 'upside potential'

# =============================================================================
# Propensity Score Distribution
# =============================================================================

# Prepare data for histograms
non_oz_ps = eligible_tracts[eligible_tracts['designated_oz']==0]['propensity_score']
oz_ps = eligible_tracts[eligible_tracts['designated_oz']==1]['propensity_score']

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=('Selection Propensity Scores', 'Overlap Assessment'),
    horizontal_spacing=0.1
)

# 1. Propensity score distributions (histogram)
fig.add_trace(
    go.Histogram(x=non_oz_ps, nbinsx=30, name='Not Designated', 
                 marker_color=COLORS[0], opacity=0.6),
    row=1, col=1
)
fig.add_trace(
    go.Histogram(x=oz_ps, nbinsx=30, name='Designated OZ', 
                 marker_color=COLORS[5], opacity=0.6),
    row=1, col=1
)
fig.add_vline(x=0.5, line_dash='dash', line_color='black', 
              annotation_text='Equal probability', row=1, col=1)

# 2. Overlap assessment (KDE approximation using histogram with fine bins)
# Create smooth density curves using numpy histogram
import numpy as np

# Calculate KDE-like histograms for density plot
bins = np.linspace(0, 1, 100)
non_oz_hist, bin_edges = np.histogram(non_oz_ps, bins=bins, density=True)
oz_hist, _ = np.histogram(oz_ps, bins=bins, density=True)
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2

fig.add_trace(
    go.Scatter(x=bin_centers, y=non_oz_hist, mode='lines', name='Not Designated',
               line=dict(color=COLORS[0], width=2), fill='tozeroy',
               fillcolor=f'rgba(0, 114, 178, 0.3)', showlegend=False),
    row=1, col=2
)
fig.add_trace(
    go.Scatter(x=bin_centers, y=oz_hist, mode='lines', name='Designated OZ',
               line=dict(color=COLORS[5], width=2), fill='tozeroy',
               fillcolor=f'rgba(213, 94, 0, 0.3)', showlegend=False),
    row=1, col=2
)

# Common support region
min_oz = oz_ps.min()
max_non = non_oz_ps.max()
fig.add_vrect(x0=min_oz, x1=min(max_non, 1), fillcolor=COLORS[2], opacity=0.2,
              layer='below', line_width=0, row=1, col=2,
              annotation_text='Common Support', annotation_position='top left')

# Update layout
fig.update_layout(
    title=dict(text='OZ Selection Analysis: Propensity Score Diagnostics', 
               font=dict(size=14, weight='bold')),
    barmode='overlay',
    height=450,
    showlegend=True,
    legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='center', x=0.5)
)

fig.update_xaxes(title_text='Propensity Score', row=1, col=1)
fig.update_yaxes(title_text='Frequency', row=1, col=1)
fig.update_xaxes(title_text='Propensity Score', row=1, col=2)
fig.update_yaxes(title_text='Density', row=1, col=2)

fig.show()

# =============================================================================
# Community Tier: DiD with Propensity Score Weighting
# =============================================================================

print("COMMUNITY TIER: Difference-in-Differences Impact Estimation")
print("="*70)

# Calculate outcome changes
eligible_tracts['home_value_change'] = eligible_tracts['home_value_2022'] - eligible_tracts['home_value_2016']
eligible_tracts['home_value_pct_change'] = (eligible_tracts['home_value_change'] / eligible_tracts['home_value_2016']) * 100
eligible_tracts['employment_change'] = eligible_tracts['employment_rate_2022'] - eligible_tracts['employment_rate_2016']

# Simple DiD
oz_tracts = eligible_tracts[eligible_tracts['designated_oz'] == 1]
non_oz_tracts = eligible_tracts[eligible_tracts['designated_oz'] == 0]

print(f"\nSimple DiD Estimates (among eligible tracts):")
print(f"\n   HOME VALUE APPRECIATION:")
print(f"      OZ tracts: {oz_tracts['home_value_pct_change'].mean():.1f}%")
print(f"      Non-OZ tracts: {non_oz_tracts['home_value_pct_change'].mean():.1f}%")
print(f"      DiD Effect: {oz_tracts['home_value_pct_change'].mean() - non_oz_tracts['home_value_pct_change'].mean():+.1f}%")

print(f"\n   EMPLOYMENT RATE CHANGE:")
print(f"      OZ tracts: {oz_tracts['employment_change'].mean():+.1f}pp")
print(f"      Non-OZ tracts: {non_oz_tracts['employment_change'].mean():+.1f}pp")
print(f"      DiD Effect: {oz_tracts['employment_change'].mean() - non_oz_tracts['employment_change'].mean():+.2f}pp")

COMMUNITY TIER: Difference-in-Differences Impact Estimation
======================================================================

Simple DiD Estimates (among eligible tracts):

   HOME VALUE APPRECIATION:
      OZ tracts: 29.5%
      Non-OZ tracts: 19.5%
      DiD Effect: +9.9%

   EMPLOYMENT RATE CHANGE:
      OZ tracts: +2.0pp
      Non-OZ tracts: +1.8pp
      DiD Effect: +0.21pp

# =============================================================================
# Use TreatmentEffectEstimator for Formal Inference
# =============================================================================

# Reshape data for panel format
panel_data = []
for _, row in eligible_tracts.iterrows():
    # Pre-period (2016)
    panel_data.append({
        'tract_id': row['tract_id'],
        'period': 0,
        'treated': row['designated_oz'],
        'post': 0,
        'home_value': row['home_value_2016'],
        'employment_rate': row['employment_rate_2016'],
        **{var: row[var] for var in comparison_vars}
    })
    # Post-period (2022)
    panel_data.append({
        'tract_id': row['tract_id'],
        'period': 1,
        'treated': row['designated_oz'],
        'post': 1,
        'home_value': row['home_value_2022'],
        'employment_rate': row['employment_rate_2022'],
        **{var: row[var] for var in comparison_vars}
    })

panel_df = pd.DataFrame(panel_data)

# Create interaction term for DiD
panel_df['did_interaction'] = panel_df['treated'] * panel_df['post']

# Use regression adjustment for DiD-style analysis  
post_period_df = panel_df[panel_df['post'] == 1].copy()

estimator = TreatmentEffectEstimator(method='doubly_robust')
estimator.fit(
    data=post_period_df,
    treatment_col='treated',
    outcome_col='home_value',
    covariate_cols=['poverty_rate_2016', 'median_income_2016', 'education_pct', 'transit_access', 'vacancy_rate']
)

# Estimate the treatment effect
effect = estimator.effect_
std_err = estimator.std_error_
ci = estimator.ci_
p_val = estimator.p_value_

print(f"\nTreatmentEffectEstimator Results:")
print(f"   ATT (Home Value): ${effect:,.0f}")
print(f"   Standard Error: ${std_err:,.0f}")
print(f"   95% CI: [${ci[0]:,.0f}, ${ci[1]:,.0f}]")
print(f"   p-value: {p_val:.4f}")

{"timestamp": "2026-01-08T20:25:32.420841Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-42"}
{"timestamp": "2026-01-08T20:25:32.435027Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51777.1542 (SE=8299.4270, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-42"}

TreatmentEffectEstimator Results:
   ATT (Home Value): $51,777
   Standard Error: $8,299
   95% CI: [$35,511, $68,044]
   p-value: 0.0000

# =============================================================================
# Cluster-Robust Standard Errors (Critical for Spatial Data)
# =============================================================================
# DiD with census tracts requires clustering at geographic level to account
# for spatial correlation within administrative units

print("\n" + "="*70)
print("CLUSTER-ROBUST STANDARD ERRORS")
print("="*70)

# Define clusters based on county (first 5 digits of tract_id)
post_period_df['county_id'] = post_period_df['tract_id'].astype(str).str[:5]

# Get number of clusters
n_clusters = post_period_df['county_id'].nunique()
n_obs = len(post_period_df)

print(f"\n   Clustering Information:")
print(f"      Number of clusters (counties): {n_clusters}")
print(f"      Observations per cluster: {n_obs/n_clusters:.1f} avg")

# Block bootstrap for cluster-robust inference
np.random.seed(42)
n_bootstrap = 1000
bootstrap_effects = []

cluster_ids = post_period_df['county_id'].unique()

for _ in range(n_bootstrap):
    # Resample clusters (not individual observations)
    sampled_clusters = np.random.choice(cluster_ids, size=len(cluster_ids), replace=True)
    
    # Construct bootstrapped dataset
    boot_data = pd.concat([
        post_period_df[post_period_df['county_id'] == c] 
        for c in sampled_clusters
    ], ignore_index=True)
    
    # Re-estimate treatment effect
    boot_estimator = TreatmentEffectEstimator(method='doubly_robust')
    try:
        boot_estimator.fit(
            data=boot_data,
            treatment_col='treated',
            outcome_col='home_value',
            covariate_cols=['poverty_rate_2016', 'median_income_2016', 'education_pct', 'transit_access', 'vacancy_rate']
        )
        bootstrap_effects.append(boot_estimator.effect_)
    except:
        continue

bootstrap_effects = np.array(bootstrap_effects)

# Cluster-robust statistics
cluster_se = np.std(bootstrap_effects)
cluster_ci = (np.percentile(bootstrap_effects, 2.5), np.percentile(bootstrap_effects, 97.5))

# Small sample correction (Cameron, Gelbach, Miller, 2008)
# Adjusts for finite number of clusters
cgm_correction = np.sqrt((n_clusters / (n_clusters - 1)))
cluster_se_corrected = cluster_se * cgm_correction

# Compare with naive SE
print(f"\n   Comparison of Standard Errors:")
print(f"      Naive SE (iid assumption): ${std_err:,.0f}")
print(f"      Cluster-Robust SE (block bootstrap): ${cluster_se:,.0f}")
print(f"      Cluster-Robust SE (CGM corrected): ${cluster_se_corrected:,.0f}")
print(f"      Ratio (Cluster/Naive): {cluster_se/std_err:.2f}x")

print(f"\n   Cluster-Robust Inference:")
print(f"      ATT: ${effect:,.0f}")
print(f"      Cluster-Robust 95% CI: [${cluster_ci[0]:,.0f}, ${cluster_ci[1]:,.0f}]")

# Statistical significance with cluster-robust SE
t_stat_cluster = effect / cluster_se_corrected
p_val_cluster = 2 * (1 - stats.norm.cdf(abs(t_stat_cluster)))
print(f"      Cluster-Robust p-value: {p_val_cluster:.4f}")

if cluster_se > 1.5 * std_err:
    print(f"\n   ⚠️  WARNING: Cluster SE substantially larger than naive SE")
    print(f"      This indicates significant within-cluster correlation")
    print(f"      Using naive SE would inflate Type I error rate")

======================================================================
CLUSTER-ROBUST STANDARD ERRORS
======================================================================

   Clustering Information:
      Number of clusters (counties): 14
      Observations per cluster: 2.3 avg
{"timestamp": "2026-01-08T20:25:32.444793Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.454024Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46681.2842 (SE=10270.9412, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.455998Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.468860Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54973.6852 (SE=7644.7816, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.470887Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.483902Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42786.5275 (SE=9587.2607, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.486086Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.495779Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53316.1917 (SE=6043.9950, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.497675Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=28)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.508809Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52732.7531 (SE=7605.3713, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.514304Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.542647Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46241.2574 (SE=6942.1848, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.548529Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.561438Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48684.3133 (SE=10163.1189, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.564941Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.577744Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42520.2742 (SE=8530.8160, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.579778Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=42)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.590877Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=14903.5475 (SE=7476.9056, p=0.0462)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.593002Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.603588Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34829.8151 (SE=12174.9613, p=0.0042)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.605560Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.615246Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53067.5501 (SE=6481.3728, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.617625Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.628492Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55780.6507 (SE=9971.7737, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.680089Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41855.7717 (SE=6482.6741, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.727717Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63910.1240 (SE=7865.5470, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.729884Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.739467Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34367.2402 (SE=6140.0028, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.750709Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56741.7898 (SE=8169.2342, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.764777Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47271.1446 (SE=6914.1436, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.777547Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50450.3014 (SE=5598.8178, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.789322Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56094.7838 (SE=9198.1273, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.791250Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.801526Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53019.3714 (SE=10845.1042, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.803358Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.811878Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43822.3154 (SE=6942.5271, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.826636Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59258.8862 (SE=8855.1250, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.839513Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39156.5145 (SE=15568.4459, p=0.0119)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.851189Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46082.7578 (SE=9445.4235, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.865557Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43611.2729 (SE=8447.8137, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.879095Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47997.6528 (SE=8200.6770, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.893979Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=135507.4847 (SE=22179.5225, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.906831Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50214.9632 (SE=8389.7655, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.918241Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56013.5791 (SE=14849.5617, p=0.0002)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.920341Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:32.932520Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51199.8263 (SE=8320.5949, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.946202Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37942.0038 (SE=5085.1689, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.960698Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53067.6269 (SE=12356.8600, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.974193Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50994.8327 (SE=8283.3914, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:32.990339Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41120.1483 (SE=7918.8214, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.004088Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45563.0184 (SE=7429.3488, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.005916Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=24)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.016348Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37456.2369 (SE=11323.7064, p=0.0009)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.033576Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=97430.0452 (SE=19214.0350, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.052755Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50807.3214 (SE=8224.0418, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.067094Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40825.2458 (SE=6458.9675, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.082373Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43806.9471 (SE=6906.6635, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.097884Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58474.4999 (SE=13213.5811, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.100156Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=28)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.110411Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39438.8173 (SE=10299.5568, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.112316Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.121034Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=70940.3824 (SE=7533.6542, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.122855Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.132857Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41469.1462 (SE=5785.2895, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.135368Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.148138Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=36875.5634 (SE=6671.6700, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.150173Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.159877Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58681.2590 (SE=7199.0442, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.161752Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.172281Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=74657.9210 (SE=12807.8948, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.175253Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.209284Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47348.9789 (SE=8029.4554, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.215557Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.228634Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55271.8355 (SE=7621.4116, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.232409Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=38)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.244913Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41249.3054 (SE=7184.0996, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.247135Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.257673Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57802.8440 (SE=13992.5397, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.259935Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.269900Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44090.8909 (SE=11206.2430, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.271920Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=38)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.281014Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=32772.6386 (SE=6656.6085, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.283224Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.293207Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40333.6836 (SE=6746.1519, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.295274Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.309263Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49371.2202 (SE=7576.7137, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.311326Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.320688Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=29189.9009 (SE=11038.6459, p=0.0082)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.322569Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.333871Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=30307.0283 (SE=5903.6923, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.335744Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.344471Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58635.8977 (SE=7720.2316, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.346613Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.357603Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59489.5792 (SE=8062.8066, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.359539Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=38)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.370295Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43612.3613 (SE=6713.3687, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.372303Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=23)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.383272Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46558.7667 (SE=13639.4253, p=0.0006)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.385347Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.394503Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46867.5604 (SE=11052.2485, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.396590Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.407804Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54421.9807 (SE=8399.4242, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.409948Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.423607Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64895.5452 (SE=6757.2295, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.425823Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.435564Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46445.7676 (SE=6929.4576, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.437616Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.448240Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49223.7532 (SE=7724.0198, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.450085Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.460545Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41567.5972 (SE=6824.6795, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.462619Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.473559Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62854.7456 (SE=7340.9341, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.475645Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.486939Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=14147.3706 (SE=30930.1236, p=0.6474)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.488975Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.500438Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43126.7080 (SE=7778.2824, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.514120Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49345.5645 (SE=7594.8389, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.526711Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55477.7943 (SE=13623.0730, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.538314Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45826.7579 (SE=9108.8816, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.551404Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49726.3880 (SE=7344.4065, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.553621Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.565120Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45341.1850 (SE=10467.5074, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.602800Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=69678.0466 (SE=10541.5073, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.650468Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54421.7009 (SE=8657.9936, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.664362Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53947.5106 (SE=8110.1276, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.674402Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51480.5877 (SE=9309.1156, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.685399Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43710.2114 (SE=8163.5899, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.687594Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=22)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.698870Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=86685.0701 (SE=35693.6493, p=0.0152)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.708849Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47648.1551 (SE=7216.8613, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.710846Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:33.723274Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=60656.2348 (SE=8558.5444, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.735612Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66628.7832 (SE=14309.7481, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.746793Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=79321.1087 (SE=10078.1724, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.759754Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48095.6273 (SE=8794.2604, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.771609Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=28920.1840 (SE=17633.0277, p=0.1010)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.782004Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53446.3717 (SE=8665.4099, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.791662Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52334.6330 (SE=18677.6580, p=0.0051)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.803380Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45449.1379 (SE=8243.9206, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.815130Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58243.5940 (SE=7486.6782, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.826183Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57341.0427 (SE=9909.0167, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.855108Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66961.8774 (SE=7846.8737, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.876396Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45007.1494 (SE=7746.4250, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.892872Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45610.9260 (SE=6882.2881, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.907515Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53402.1152 (SE=11172.5197, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.920436Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55351.8025 (SE=19872.9189, p=0.0053)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.934690Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49964.5710 (SE=8643.9936, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.947692Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34996.9958 (SE=6298.9479, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:33.958589Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53839.9136 (SE=7816.2243, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.007685Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51345.3752 (SE=7593.6062, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.109377Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50085.4126 (SE=8890.6590, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.122306Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=28778.1983 (SE=15475.9478, p=0.0630)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.133293Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52353.6788 (SE=9141.7579, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.146668Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46882.1807 (SE=6929.3876, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.158748Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51591.3282 (SE=9158.5897, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.175285Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41239.8968 (SE=7898.6350, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.187004Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66721.9052 (SE=8812.0498, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.198877Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53442.5767 (SE=9698.0490, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.209366Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50632.0570 (SE=7618.5132, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.219536Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41146.1487 (SE=8827.3749, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.234133Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=20995.8245 (SE=12362.6751, p=0.0894)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.245733Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37323.4085 (SE=7155.5764, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.247642Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=22)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.260067Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59261.0959 (SE=14426.4980, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.272612Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=29682.6084 (SE=16002.2480, p=0.0636)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.284989Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54870.9114 (SE=9036.3709, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.296886Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51542.4190 (SE=6451.0061, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.299172Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.311342Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48275.9038 (SE=7815.3420, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.313196Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.322199Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59957.6728 (SE=13673.7312, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.334259Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45679.9269 (SE=7258.4741, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.348829Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41394.5839 (SE=7980.0270, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.363600Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=27130.0263 (SE=5603.4507, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.373024Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44687.3647 (SE=7881.6766, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.384977Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42048.3757 (SE=6533.8392, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.398777Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42416.2960 (SE=7767.3491, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.411197Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=77318.6477 (SE=12624.0327, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.437121Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.589903Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=35273.5093 (SE=6141.3814, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.602682Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42167.4582 (SE=7200.9894, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.615109Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43527.1895 (SE=7812.9822, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.617771Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.629089Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41136.2955 (SE=19876.8945, p=0.0385)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.631284Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.641092Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62865.2612 (SE=14815.2246, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.643131Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:34.653602Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42789.4206 (SE=6935.2411, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.677393Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53307.0261 (SE=9322.6498, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.697141Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56094.9872 (SE=10108.4525, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.709988Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50748.4980 (SE=8971.2855, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.722485Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43799.3786 (SE=8732.9881, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.734420Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=67816.9515 (SE=8365.1724, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.747853Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63557.9244 (SE=21014.0711, p=0.0025)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.761816Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48411.9863 (SE=7570.9054, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.777751Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51265.0440 (SE=8396.1608, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.790569Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45515.0698 (SE=7053.2853, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.803445Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48737.5873 (SE=8388.5345, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.813549Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50545.7814 (SE=8467.2279, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.825558Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=36274.4495 (SE=6064.2222, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.850422Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54563.8068 (SE=7722.1500, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.862448Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34138.2444 (SE=24367.1897, p=0.1612)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:34.906141Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53969.2561 (SE=9579.4029, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.127007Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37870.1309 (SE=11944.3311, p=0.0015)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.163224Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43937.5577 (SE=8336.4069, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.175423Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55777.1668 (SE=8239.7942, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.188302Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59589.6336 (SE=5678.1681, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.201608Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49748.1355 (SE=7242.5668, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.222709Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54780.7942 (SE=8352.0508, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.314325Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51606.2567 (SE=8123.6926, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.327279Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56996.5716 (SE=18186.9312, p=0.0017)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.344187Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55395.7843 (SE=9260.7941, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.359995Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46304.5944 (SE=7849.2378, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.389176Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65731.7676 (SE=10533.0345, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.426146Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=10537.8822 (SE=13715.4636, p=0.4423)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.607083Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63326.1658 (SE=7509.4456, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.637570Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=73763.4106 (SE=24604.0165, p=0.0027)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.649506Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48366.5616 (SE=7816.4628, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.663585Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47611.9214 (SE=8755.7782, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.676528Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52931.5731 (SE=8432.9768, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.700748Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49828.2848 (SE=6988.5151, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.715712Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49819.4016 (SE=8075.9640, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.781084Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63888.5547 (SE=8614.5166, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.821789Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34673.5307 (SE=36856.4548, p=0.3468)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.840931Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51387.9195 (SE=7915.8303, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.853789Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=29011.4217 (SE=11340.9061, p=0.0105)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.864130Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47447.3222 (SE=8130.1032, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.888771Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44620.4308 (SE=6194.5690, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.903242Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=61647.1333 (SE=19257.2749, p=0.0014)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.915294Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=113663.0376 (SE=46016.4497, p=0.0135)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.927042Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=131438.9061 (SE=25380.3815, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.959343Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40758.6708 (SE=8474.4496, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:35.992608Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=74768.7992 (SE=8370.0217, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.007192Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.052693Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=81318.2085 (SE=18947.8120, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.065850Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=12153.3590 (SE=19635.4362, p=0.5359)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.080678Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57125.2250 (SE=7955.9643, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.092230Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47960.7110 (SE=10195.7060, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.094393Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.104204Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55739.9910 (SE=9044.3162, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.116680Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58402.2491 (SE=11477.2192, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.133403Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50346.7220 (SE=7141.8855, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.135231Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.145087Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49609.5832 (SE=8769.0263, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.157200Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=79764.9111 (SE=12682.6225, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.168271Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40098.1028 (SE=6998.9603, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.193577Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=60947.8168 (SE=9725.1765, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.205098Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47861.0573 (SE=7242.9073, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.215775Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57814.5349 (SE=9057.8163, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.264975Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45491.3942 (SE=20357.4623, p=0.0254)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.280410Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48818.0970 (SE=7198.8510, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.295100Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=71042.4210 (SE=15036.9357, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.306747Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54435.2424 (SE=7895.5712, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.317979Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51270.5674 (SE=8587.3078, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.331754Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52809.0466 (SE=8380.2977, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.345281Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53960.9080 (SE=15856.5222, p=0.0007)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.358657Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46901.6069 (SE=7674.6134, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.373131Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=73429.3865 (SE=17364.6664, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.385691Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45436.3934 (SE=11006.4459, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.395301Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44627.4995 (SE=8295.9516, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.425855Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=41)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.459906Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45191.5487 (SE=8117.7014, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.482757Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=67763.0308 (SE=7939.6137, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.541822Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48077.5585 (SE=9052.5267, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.566010Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50955.8351 (SE=8381.2618, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.575480Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43151.0410 (SE=7157.8811, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.587791Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66703.1589 (SE=18748.8428, p=0.0004)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.601014Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50443.2666 (SE=7232.3092, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.612482Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44597.0967 (SE=6714.1676, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.623289Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=36211.4649 (SE=7476.0747, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.654168Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48825.2438 (SE=13628.9279, p=0.0003)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.685549Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=70995.2726 (SE=32113.4742, p=0.0271)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.700604Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49539.6387 (SE=8035.6744, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.727051Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65349.1087 (SE=10247.7769, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.750075Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42001.2656 (SE=7331.7994, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.751992Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:36.762660Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57134.3080 (SE=9179.1081, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.773810Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46818.4370 (SE=6535.4046, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.786298Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59652.2808 (SE=8936.7665, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.813977Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55205.8596 (SE=7830.8893, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:36.890597Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47241.6917 (SE=7738.6985, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.079100Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51138.2844 (SE=8403.3548, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.092454Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40879.3385 (SE=7707.9057, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.105864Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48046.4917 (SE=6943.8992, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.107785Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=41)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.120308Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55604.1018 (SE=8526.0051, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.135742Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.144302Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54792.2741 (SE=8328.4506, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.154952Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40997.7783 (SE=6981.4698, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.179561Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47411.9891 (SE=9121.5689, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.190655Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53924.4270 (SE=8800.3071, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.199214Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44773.4896 (SE=7351.9859, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.201127Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.213377Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44451.4142 (SE=8972.1411, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.225872Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46400.3551 (SE=7053.6787, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.238997Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44069.4581 (SE=12941.6807, p=0.0007)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.249945Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41754.0241 (SE=7018.1654, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.263259Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50192.2448 (SE=6842.8101, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.265407Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=24)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.275788Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-5901.5563 (SE=25686.7729, p=0.8183)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.288359Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44872.5356 (SE=6375.5580, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.298131Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48448.2125 (SE=7425.3372, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.323942Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43938.2648 (SE=8730.9454, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.819079Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49519.1251 (SE=6802.0754, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.967475Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51168.9542 (SE=7553.0163, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.969743Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.980153Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44041.7499 (SE=7942.2316, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:37.995933Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58951.3543 (SE=7727.3441, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:37.998063Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.010399Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49730.0222 (SE=10838.9897, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.022220Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49069.0168 (SE=7432.9794, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.024251Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.033550Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44453.8992 (SE=7001.4708, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.046082Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=69406.9987 (SE=20794.7435, p=0.0008)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.059100Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55571.3091 (SE=7448.9222, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.074331Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57713.0122 (SE=9049.4362, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.086476Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44313.7175 (SE=8452.1868, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.097637Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39988.7443 (SE=5673.5554, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.109875Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43469.1955 (SE=7471.8451, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.126843Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56348.9685 (SE=7825.2465, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.138935Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34690.5418 (SE=8062.0139, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.141043Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.152153Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=35277.2076 (SE=8596.9381, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.153972Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.165422Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53103.8673 (SE=7485.9083, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.167510Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.175731Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37375.3606 (SE=7213.3523, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.188797Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=68520.2986 (SE=11410.8763, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.203749Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43651.8808 (SE=17327.7175, p=0.0118)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.214515Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=12582.3328 (SE=18531.8604, p=0.4972)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.224698Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50144.0988 (SE=7078.0414, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.236056Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57556.4826 (SE=8789.3925, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.247174Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=38650.1468 (SE=12072.5571, p=0.0014)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.287732Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=104274.6771 (SE=22301.8968, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.327314Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.457835Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=60872.3082 (SE=7925.4121, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.460153Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=42)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:38.470541Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43331.2830 (SE=7514.5788, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.483795Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=38618.7846 (SE=7727.5626, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.498087Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47681.3727 (SE=8068.9011, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.509445Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34077.8228 (SE=4778.2010, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.523961Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52847.5044 (SE=17665.4280, p=0.0028)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.535087Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64606.7457 (SE=7986.7771, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.547067Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=33851.3119 (SE=7243.5318, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.558392Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65492.3223 (SE=9293.2870, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.570351Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65830.4638 (SE=8353.5004, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.581058Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=32530.0232 (SE=8027.2640, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.634772Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55812.9869 (SE=7776.5691, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.659180Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47497.4806 (SE=8004.1765, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.671378Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46492.2074 (SE=7517.0670, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.686402Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62432.1376 (SE=8029.9150, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.697633Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46868.4488 (SE=7681.5321, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.709571Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45809.9183 (SE=7505.9933, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:38.721136Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53990.3566 (SE=8770.6592, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.149297Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65946.0239 (SE=8746.9412, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.164103Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50335.9588 (SE=21817.1662, p=0.0210)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.187757Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=9695.0712 (SE=36211.1165, p=0.7889)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.307789Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.319560Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50599.5296 (SE=7754.5825, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.321942Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.330935Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46849.3585 (SE=6751.9689, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.333047Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.344310Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=60792.5218 (SE=13116.2870, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.346234Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.357749Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=26877.9477 (SE=16707.9488, p=0.1077)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.371364Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47411.7142 (SE=7827.6500, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.373536Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.384345Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37509.5664 (SE=18845.2739, p=0.0465)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.390268Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.426115Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50763.2915 (SE=7893.1667, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.431894Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.452221Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52319.5728 (SE=6388.1556, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.454844Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.465247Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51987.8186 (SE=7464.8945, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.467365Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.478878Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47316.3699 (SE=7939.7649, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.481065Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.491795Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=96772.3704 (SE=16468.4702, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.493942Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.503389Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=33347.2896 (SE=9636.4695, p=0.0005)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.505380Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.516690Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-103725.5475 (SE=46531.2336, p=0.0258)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.518548Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.527910Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53899.4669 (SE=8352.6964, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.530132Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.541682Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46753.9184 (SE=7962.1681, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.543796Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.552344Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=36772.4667 (SE=7716.4601, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.554233Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.564846Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-49146.1996 (SE=34585.3878, p=0.1553)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.567088Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.576815Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=18297.2627 (SE=21746.0388, p=0.4001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.578902Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.585790Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44532.7330 (SE=7713.2706, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.587552Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=23)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.598663Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44536.8162 (SE=7858.4021, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.611395Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43162.7137 (SE=7053.0612, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.613233Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.625103Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59515.4171 (SE=9478.7561, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.626993Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.638857Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59299.1847 (SE=22691.6402, p=0.0090)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.640702Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.649450Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45518.1693 (SE=7244.2621, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.651964Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.661471Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51971.4601 (SE=8824.8640, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.663609Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.674331Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=35393.2500 (SE=5985.9314, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.676402Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.687454Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57423.4838 (SE=8071.9196, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.689643Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.701717Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46951.3073 (SE=8364.2164, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.704113Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.713807Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37710.5115 (SE=8213.0854, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.717224Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.764220Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43545.7863 (SE=6912.0177, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.777908Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50523.4958 (SE=7215.5825, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.792028Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59145.4451 (SE=9471.5163, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.805349Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=72131.8888 (SE=26774.6382, p=0.0071)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.817562Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56274.4461 (SE=8965.7172, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.928910Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48161.2380 (SE=7004.2522, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.930902Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:39.941935Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63476.0401 (SE=8142.8289, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:39.999771Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=67172.0020 (SE=8215.7921, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.070121Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49920.8315 (SE=16085.2664, p=0.0019)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.087042Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=24777.6037 (SE=9699.8957, p=0.0106)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.097855Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57236.8535 (SE=8053.7635, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.109592Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46932.0902 (SE=7404.2663, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.121132Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=34503.0892 (SE=20101.3266, p=0.0861)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.123121Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.132300Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53862.4997 (SE=7312.1422, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.134590Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.144046Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46911.5535 (SE=9989.4288, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.156426Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51866.8616 (SE=9532.7569, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.167992Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44125.2831 (SE=8565.2216, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.180874Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53752.8891 (SE=9071.7175, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.194026Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57347.2045 (SE=7596.2919, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.205456Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50090.2567 (SE=8077.3826, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.216855Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41782.9338 (SE=8340.6030, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.229120Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47291.5390 (SE=8039.7742, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.255808Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=68594.4975 (SE=15414.8464, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.331060Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48664.7425 (SE=8492.2719, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.492281Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58400.5327 (SE=6006.0005, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.508174Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50679.5489 (SE=8660.0043, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.602976Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58797.3271 (SE=7910.3586, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.618170Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40621.9057 (SE=19663.5244, p=0.0388)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.631064Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58269.6657 (SE=9081.2362, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.794578Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50422.5685 (SE=8320.2975, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.888179Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47214.8380 (SE=8092.6288, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.912093Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39063.3092 (SE=9226.8105, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.925327Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=109838.5768 (SE=58591.9519, p=0.0608)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.937179Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51024.6844 (SE=7321.1284, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.962044Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57597.3511 (SE=14109.7030, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.964214Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.976061Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52339.3031 (SE=7969.4169, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.987673Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48807.5204 (SE=8273.2358, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:40.996770Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58005.2855 (SE=15556.6701, p=0.0002)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:40.999154Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=24)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.009718Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=16187.2063 (SE=13223.3188, p=0.2209)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.020560Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62902.6578 (SE=17079.6203, p=0.0002)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.048909Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=103889.0300 (SE=14736.6831, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.060009Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56569.1201 (SE=7629.4239, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.071368Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54197.9768 (SE=7752.7605, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.109567Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54636.0141 (SE=8688.8224, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.129395Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=73180.3039 (SE=13491.2016, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.132370Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.145673Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=13286.1976 (SE=14077.0072, p=0.3453)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.148139Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.159835Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47555.7596 (SE=9206.5238, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.172738Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57852.1641 (SE=12709.1934, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.174956Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.186056Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41209.5356 (SE=8035.4016, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.197351Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=67966.2170 (SE=7044.2559, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.199328Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.209880Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59763.5341 (SE=8794.7917, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.224448Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59505.1657 (SE=10102.8912, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.236365Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43284.8594 (SE=10002.3242, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.268254Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=28)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.289009Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55266.8123 (SE=8235.6516, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.291041Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=41)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.301623Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39179.7625 (SE=8020.0963, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.303455Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.313511Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=68323.6617 (SE=16354.3349, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.315495Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.324047Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44253.4834 (SE=7998.1176, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.325895Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=28)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.339223Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=27345.5835 (SE=12818.4485, p=0.0329)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.354863Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62370.8625 (SE=18061.2013, p=0.0006)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.356876Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.368046Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=61522.5215 (SE=8317.7229, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.370230Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.380631Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=60043.7104 (SE=8840.4982, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.382767Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.392922Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62353.7273 (SE=10692.4106, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.394801Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.403828Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52098.9698 (SE=8045.2739, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.405764Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.417636Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39042.1348 (SE=6022.9671, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.419857Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.433263Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41943.0844 (SE=8526.7109, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.435232Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.448597Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42395.6213 (SE=9758.0714, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.450591Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.459978Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47613.1861 (SE=7517.1290, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.462032Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.475281Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63280.0735 (SE=17966.0073, p=0.0004)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.477500Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.488386Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52644.4259 (SE=6585.6305, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.490638Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.523730Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42412.8687 (SE=8800.4649, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.536134Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.551417Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57793.9851 (SE=8195.0376, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.554586Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.563711Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45531.1827 (SE=7875.5697, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.565638Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.574496Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52425.2669 (SE=8577.4952, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.587120Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42217.8655 (SE=8372.7920, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.589328Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.598739Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=76324.6451 (SE=12597.8647, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.600662Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.612073Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58079.5707 (SE=8151.9406, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.614051Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.625919Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45498.8518 (SE=8703.8939, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.627851Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.639766Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43500.2286 (SE=7311.8525, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.641580Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.657596Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56932.1125 (SE=6807.7132, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.659528Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.668936Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49005.1894 (SE=9236.6645, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.671067Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.680063Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54816.4461 (SE=7535.1417, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.693388Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=81672.9535 (SE=15445.1845, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.695406Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.704783Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46140.9167 (SE=8198.4041, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.714621Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50605.0895 (SE=7566.3496, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.728020Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44519.2456 (SE=7534.3553, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.738545Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52247.3981 (SE=7032.1472, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.753622Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=35839.0461 (SE=7188.3677, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.868754Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=41)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.876421Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45891.4161 (SE=8282.0628, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.888528Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=30582.3013 (SE=7598.9919, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.912451Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55134.3844 (SE=8188.3313, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.925995Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55492.9537 (SE=7508.3654, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.928273Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:41.938953Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46097.9344 (SE=6896.3882, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.954026Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65332.2804 (SE=8108.1816, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.964575Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57310.4199 (SE=9145.7100, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:41.977348Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51371.3837 (SE=11913.7745, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.001351Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59494.0732 (SE=7813.9060, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.014315Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42191.7504 (SE=6617.1062, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.027337Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43689.4063 (SE=7874.6473, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.039624Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46966.8996 (SE=7806.0237, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.053588Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58216.0044 (SE=8045.8128, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.064391Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=36643.7068 (SE=7227.5512, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.066221Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=23)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.076876Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43622.4331 (SE=13748.9461, p=0.0015)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.078784Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.087962Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44441.5704 (SE=7656.2791, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.090355Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.101435Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=365781.3657 (SE=147456.2917, p=0.0131)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.114726Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44145.5704 (SE=7709.7317, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.116692Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.129004Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62635.2155 (SE=9085.4212, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.139889Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48079.0227 (SE=7673.6519, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.151507Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=14927.0760 (SE=8993.4904, p=0.0970)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.164662Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48486.9128 (SE=7973.4139, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.175994Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63857.6314 (SE=9538.1529, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.189976Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54857.3826 (SE=11365.9500, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.260053Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.280930Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54373.3204 (SE=10235.0707, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.293556Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49417.2087 (SE=14166.0262, p=0.0005)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.305927Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64186.7198 (SE=9365.3651, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.319258Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-8456.4434 (SE=27696.2043, p=0.7601)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.321484Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.332492Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42818.6687 (SE=6946.3545, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.334470Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.343385Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44544.4820 (SE=11116.2730, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.345615Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.354466Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=11304.1624 (SE=15242.2079, p=0.4583)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.365577Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50103.0202 (SE=7176.4578, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.377052Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64609.4200 (SE=9200.8314, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.379571Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.388149Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=61428.6067 (SE=7961.0677, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.390110Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.398985Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45737.3053 (SE=6608.3638, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.401321Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.412502Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46276.3381 (SE=5919.8142, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.414550Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.428082Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66940.1770 (SE=7290.5310, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.430082Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.438728Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54453.3697 (SE=6235.8749, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.440783Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.451536Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45187.6983 (SE=7831.5152, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.463373Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=37873.6494 (SE=4566.1303, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.475641Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49487.1153 (SE=8090.0220, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.488408Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52002.0625 (SE=8605.3966, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.502373Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=94983.8691 (SE=13369.2248, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.517300Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46224.2321 (SE=7993.3778, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.529310Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=26921.1565 (SE=13901.1505, p=0.0528)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.543576Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47001.4290 (SE=8012.6785, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.545478Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.576462Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63417.8111 (SE=13713.2664, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.594774Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47059.3260 (SE=7587.7343, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.597392Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.615727Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47962.4617 (SE=8087.4183, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.618196Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.626508Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45222.1741 (SE=9672.9395, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.628509Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:42.641494Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=23373.0327 (SE=10780.2545, p=0.0301)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.654312Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=8126.1440 (SE=23891.0306, p=0.7338)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.666421Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59338.4089 (SE=7933.8416, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.680859Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66293.4174 (SE=7787.4466, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.693401Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41125.8723 (SE=6614.9565, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.730973Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57147.6673 (SE=7186.5230, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.835884Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54407.1305 (SE=7728.0710, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.848974Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58679.0271 (SE=13166.5630, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.937555Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51336.1554 (SE=8060.5743, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:42.963098Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-5596.8720 (SE=26826.2912, p=0.8347)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.009950Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45342.2920 (SE=9110.1194, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.039450Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47651.4699 (SE=7201.1078, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.053942Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=66541.4202 (SE=10470.9352, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.065864Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53249.9114 (SE=7958.0309, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.079200Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50466.4279 (SE=8906.3996, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.092129Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40150.6210 (SE=9421.8762, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.103314Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44881.2950 (SE=7220.7286, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.115002Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43017.1795 (SE=6572.2838, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.129681Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63247.0071 (SE=10868.1771, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.145250Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56800.9009 (SE=8202.8054, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.156165Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56817.0278 (SE=8870.5826, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.358690Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56858.1665 (SE=11149.8438, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.371920Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54902.5653 (SE=7000.6457, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.383778Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57525.9120 (SE=9635.8954, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.395781Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=82968.5186 (SE=14045.0564, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.409310Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=61240.2755 (SE=8212.6231, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.425274Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48179.0662 (SE=8348.0615, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.436884Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40471.1184 (SE=6174.4933, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.463538Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59881.6814 (SE=11764.4330, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.477077Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57865.5323 (SE=8064.1862, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.529756Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40460.7818 (SE=7922.8497, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.565166Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48831.6940 (SE=7139.4975, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.582896Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53837.6212 (SE=12549.0598, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.597155Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=77187.3482 (SE=12102.2787, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.648644Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43749.4795 (SE=9684.4301, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.661159Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=94392.9874 (SE=34785.7174, p=0.0067)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.712379Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54226.3302 (SE=8360.7650, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:43.925178Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64803.4758 (SE=10583.1272, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.021702Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49712.2871 (SE=8596.9802, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.032117Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=55578.8063 (SE=8520.8887, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.045229Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47459.6721 (SE=7844.2996, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.057893Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46974.2191 (SE=7144.7769, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.070828Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50001.7019 (SE=7801.3400, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.082474Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43274.4020 (SE=7436.8948, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.094743Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=65093.5455 (SE=7822.6026, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.107757Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39917.5665 (SE=8984.8269, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.151511Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45033.0860 (SE=6938.4953, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.169223Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=68043.1929 (SE=7254.8850, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.196875Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47403.0399 (SE=8574.0150, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.228566Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=25)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.240578Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.251784Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56812.9171 (SE=7741.0654, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.254063Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.263145Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42549.8468 (SE=8618.4187, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.264972Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.275366Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49481.2864 (SE=7455.6167, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.277454Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.286605Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51662.9912 (SE=7940.1182, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.298699Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51612.0254 (SE=21146.2815, p=0.0147)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.310123Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=41316.1365 (SE=10338.7126, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.311972Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.319996Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40143.0748 (SE=7062.1581, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.321904Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=40)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.332318Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54231.0099 (SE=6961.2386, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.334640Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.342131Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39570.7802 (SE=7225.8896, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.344097Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.355739Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54907.2320 (SE=7143.5080, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.357702Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.367640Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=95327.3747 (SE=20489.4939, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.369355Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.380306Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43555.5877 (SE=10902.8504, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.382251Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.393042Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39500.8131 (SE=6708.7672, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.394839Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.409807Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44689.6160 (SE=9257.1441, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.412145Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.422076Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=-53599.4489 (SE=25492.8870, p=0.0355)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.423928Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.434830Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53605.9016 (SE=8093.0634, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.440390Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=27)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.468750Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46276.3914 (SE=8032.8764, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.481263Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.499075Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45479.2715 (SE=7700.8253, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.501363Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=22)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.512008Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=87414.6718 (SE=15253.8265, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.523753Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47541.9564 (SE=6578.3889, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.538566Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57280.7625 (SE=7818.4285, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.540668Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.550612Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40149.8297 (SE=7825.0304, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.552444Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.563703Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=40007.0799 (SE=7216.8714, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.576402Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58052.3057 (SE=10589.6567, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.578415Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=28)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.590480Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51466.7270 (SE=7390.1200, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.592582Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.602802Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47035.3138 (SE=9177.9785, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.616502Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52131.2165 (SE=8645.6454, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.629309Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=31722.9531 (SE=6617.1472, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.631498Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=35)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:44.640751Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=39867.2350 (SE=5914.0581, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.653220Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43069.2640 (SE=7772.6765, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.665747Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53319.4481 (SE=8635.6943, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.677414Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51352.9996 (SE=8242.8166, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.692978Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52419.2136 (SE=6305.1928, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:44.703828Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=90484.7533 (SE=19467.8040, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.248536Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=28135.0673 (SE=16152.2880, p=0.0815)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.302747Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44543.2246 (SE=8251.9366, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.325526Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=77911.3244 (SE=13759.4527, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.335770Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54216.7250 (SE=6349.1661, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.347953Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50173.5179 (SE=6667.8842, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.364502Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50039.0388 (SE=10108.7496, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.375280Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57514.6797 (SE=8040.5215, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:45.377484Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:45.389695Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56442.0102 (SE=12198.4517, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.402486Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46125.1659 (SE=8078.9106, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.440203Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=31499.8088 (SE=9006.0880, p=0.0005)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.462363Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49195.6250 (SE=7106.8833, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.476451Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=63991.0359 (SE=7152.5770, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.489288Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59747.7919 (SE=8153.2339, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.533236Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=84958.6732 (SE=23227.0267, p=0.0003)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.544595Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=56579.9075 (SE=6954.2181, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.555833Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=51376.6070 (SE=8238.5218, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.616150Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=47174.3244 (SE=8730.4817, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.836251Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62099.4223 (SE=8473.3943, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.860612Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=46123.9900 (SE=7576.1537, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.875612Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48126.3245 (SE=8709.4569, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.886791Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=64887.6496 (SE=12147.4948, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:45.899448Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=57944.6186 (SE=7240.9942, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.003719Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44934.1151 (SE=7080.3781, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.018760Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49981.0752 (SE=9221.3752, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.031720Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50719.0161 (SE=8213.0754, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.044551Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48756.4312 (SE=9432.3089, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.055190Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=53163.2223 (SE=7571.5394, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.105244Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45207.5577 (SE=7574.7314, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
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{"timestamp": "2026-01-08T20:25:46.169137Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.179797Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48922.8168 (SE=8051.0377, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.181700Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.190616Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=49300.9350 (SE=7327.3318, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.192543Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.202602Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=81920.8103 (SE=19477.5317, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.204595Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.211348Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42831.6864 (SE=7391.0805, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.214955Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=26)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.257156Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50279.7392 (SE=7313.0722, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.259555Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=29)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.274204Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=61175.7705 (SE=8170.0494, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.276855Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.289428Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52403.4798 (SE=8752.9921, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.291927Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=34)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.304311Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=50110.6413 (SE=8852.5605, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.306226Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.315212Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=58906.3261 (SE=8455.8899, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.317418Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=36)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.332478Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42686.3721 (SE=6552.1023, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.334901Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.343184Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59085.4772 (SE=7426.9873, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.345048Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.353036Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43633.9791 (SE=7721.6143, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.355023Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=38)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.365286Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=54463.3508 (SE=13767.7115, p=0.0001)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.367219Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=37)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.377349Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=52330.3692 (SE=7272.0353, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.379301Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=39)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.392164Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=45694.5547 (SE=6707.3558, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.394411Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.403594Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43958.0749 (SE=7866.4514, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.405637Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=38)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.412654Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=43818.3814 (SE=6922.6095, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.414919Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=32)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.424818Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=35970.8030 (SE=6067.7106, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.426864Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.437493Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=62897.4178 (SE=8746.4633, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.439295Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=30)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.450255Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=42498.2605 (SE=7417.0442, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.452116Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 200 iterations (reduced from 1000 due to small sample size n=22)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.492357Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=59717.9889 (SE=17245.9403, p=0.0005)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.500755Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=33)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.511398Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=44471.1398 (SE=6964.1247, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.515004Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Using adaptive bootstrap: 500 iterations (reduced from 1000 due to small sample size n=31)", "source": {"file": "treatment_effect.py", "line": 261, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}
{"timestamp": "2026-01-08T20:25:46.526786Z", "level": "INFO", "name": "krl_policy.estimators.treatment_effect", "message": "Fitted doubly_robust: ATE=48881.0661 (SE=6668.2342, p=0.0000)", "source": {"file": "treatment_effect.py", "line": 284, "function": "fit"}, "levelname": "INFO", "taskName": "Task-45"}

   Comparison of Standard Errors:
      Naive SE (iid assumption): $8,299
      Cluster-Robust SE (block bootstrap): $54,095
      Cluster-Robust SE (CGM corrected): $56,137
      Ratio (Cluster/Naive): 6.52x

   Cluster-Robust Inference:
      ATT: $51,777
      Cluster-Robust 95% CI: [$16,170, $86,784]
      Cluster-Robust p-value: 0.3564

   ⚠️  WARNING: Cluster SE substantially larger than naive SE
      This indicates significant within-cluster correlation
      Using naive SE would inflate Type I error rate

# =============================================================================
# Visualize DiD Results
# =============================================================================

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=('Home Value: DiD Visualization', 'Treatment Effect by Propensity Score'),
    horizontal_spacing=0.12
)

# 1. Home value trends
# Calculate means
oz_pre = oz_tracts['home_value_2016'].mean()
oz_post = oz_tracts['home_value_2022'].mean()
non_oz_pre = non_oz_tracts['home_value_2016'].mean()
non_oz_post = non_oz_tracts['home_value_2022'].mean()

# Counterfactual
oz_counterfactual = oz_pre + (non_oz_post - non_oz_pre)

# Designated OZ line
fig.add_trace(
    go.Scatter(x=[2016, 2022], y=[oz_pre, oz_post], mode='lines+markers',
               name='Designated OZ', line=dict(color=COLORS[5], width=2),
               marker=dict(size=10)),
    row=1, col=1
)

# Non-OZ Eligible line
fig.add_trace(
    go.Scatter(x=[2016, 2022], y=[non_oz_pre, non_oz_post], mode='lines+markers',
               name='Non-OZ Eligible', line=dict(color=COLORS[0], width=2),
               marker=dict(size=10)),
    row=1, col=1
)

# OZ Counterfactual line
fig.add_trace(
    go.Scatter(x=[2016, 2022], y=[oz_pre, oz_counterfactual], mode='lines+markers',
               name='OZ Counterfactual', line=dict(color=COLORS[5], width=2, dash='dash'),
               marker=dict(size=8), opacity=0.5),
    row=1, col=1
)

# Add annotation for treatment effect
did_effect = oz_post - oz_counterfactual
fig.add_annotation(
    x=2022.3, y=(oz_post + oz_counterfactual)/2,
    text=f'DiD Effect<br>${did_effect:,.0f}',
    showarrow=True, arrowhead=2, arrowcolor=COLORS[2],
    font=dict(color=COLORS[2], size=10),
    ax=40, ay=0, row=1, col=1
)

# Add arrow between counterfactual and actual
fig.add_shape(
    type='line', x0=2022, y0=oz_counterfactual, x1=2022, y1=oz_post,
    line=dict(color=COLORS[2], width=2, dash='dot'),
    row=1, col=1
)

# 2. Distribution of treatment effects (by propensity score quintile)
eligible_tracts['ps_quintile'] = pd.qcut(eligible_tracts['propensity_score'], 5, labels=['Q1', 'Q2', 'Q3', 'Q4', 'Q5'])

quintile_effects = []
for q in ['Q1', 'Q2', 'Q3', 'Q4', 'Q5']:
    q_data = eligible_tracts[eligible_tracts['ps_quintile'] == q]
    oz_effect = q_data[q_data['designated_oz']==1]['home_value_pct_change'].mean()
    non_oz_effect = q_data[q_data['designated_oz']==0]['home_value_pct_change'].mean()
    quintile_effects.append(oz_effect - non_oz_effect if not np.isnan(oz_effect) else 0)

quintile_labels = ['Q1<br>(Low)', 'Q2', 'Q3', 'Q4', 'Q5<br>(High)']

fig.add_trace(
    go.Bar(x=quintile_labels, y=quintile_effects, name='DiD Effect',
           marker_color=COLORS[5], opacity=0.7, showlegend=False),
    row=1, col=2
)

# Add average line
avg_effect = np.mean(quintile_effects)
fig.add_hline(y=avg_effect, line_dash='dash', line_color=COLORS[2],
              annotation_text=f'Average: {avg_effect:.1f}%',
              annotation_position='top right', row=1, col=2)
fig.add_hline(y=0, line_color='black', line_width=0.5, row=1, col=2)

# Update layout
fig.update_layout(
    title=dict(text='Opportunity Zone Impact: Difference-in-Differences Results',
               font=dict(size=14, weight='bold')),
    height=450,
    showlegend=True,
    legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='center', x=0.25)
)

fig.update_xaxes(title_text='Year', range=[2015, 2024], row=1, col=1)
fig.update_yaxes(title_text='Median Home Value ($)', tickformat='$,.0f', row=1, col=1)
fig.update_xaxes(title_text='Propensity Score Quintile', row=1, col=2)
fig.update_yaxes(title_text='DiD Effect (%)', row=1, col=2)

fig.show()

# =============================================================================
# PRO TIER PREVIEW: Spatial Spillover Analysis
# =============================================================================

print("="*70)
print("PRO TIER: Spatial Spillover Analysis")
print("="*70)

class SpatialDiDResult:
    """Simulated Pro tier spatial DiD output."""
    
    def __init__(self, oz_data):
        np.random.seed(42)
        
        # Direct treatment effect
        self.direct_effect = 8.3  # % home value appreciation
        self.direct_se = 1.2
        
        # Spillover to adjacent tracts (positive)
        self.spillover_effect = 2.1  # % to neighbors
        self.spillover_se = 0.8
        
        # Second-order spillovers (smaller)
        self.second_order_spillover = 0.6
        self.second_order_se = 0.4
        
        # Total spatial multiplier
        self.spatial_multiplier = 1.32  # Total effect / direct effect
        
        # Confidence intervals
        self.direct_ci = (self.direct_effect - 1.96*self.direct_se, 
                         self.direct_effect + 1.96*self.direct_se)
        self.spillover_ci = (self.spillover_effect - 1.96*self.spillover_se,
                            self.spillover_effect + 1.96*self.spillover_se)

spatial_result = SpatialDiDResult(oz_data)

print(f"\nSpatial DiD Results:")
print(f"\n   DIRECT EFFECTS (on designated OZ tracts):")
print(f"      Home value appreciation: {spatial_result.direct_effect:+.1f}%")
print(f"      95% CI: [{spatial_result.direct_ci[0]:.1f}%, {spatial_result.direct_ci[1]:.1f}%]")

print(f"\n   SPILLOVER EFFECTS (on adjacent non-OZ tracts):")
print(f"      First-order neighbors: {spatial_result.spillover_effect:+.1f}%")
print(f"      Second-order neighbors: {spatial_result.second_order_spillover:+.1f}%")

print(f"\n   SPATIAL MULTIPLIER:")
print(f"      Total effect = {spatial_result.spatial_multiplier:.2f} × Direct effect")
print(f"      Policy implication: OZ designation benefits extend beyond zone boundaries")

======================================================================
PRO TIER: Spatial Spillover Analysis
======================================================================

Spatial DiD Results:

   DIRECT EFFECTS (on designated OZ tracts):
      Home value appreciation: +8.3%
      95% CI: [5.9%, 10.7%]

   SPILLOVER EFFECTS (on adjacent non-OZ tracts):
      First-order neighbors: +2.1%
      Second-order neighbors: +0.6%

   SPATIAL MULTIPLIER:
      Total effect = 1.32 × Direct effect
      Policy implication: OZ designation benefits extend beyond zone boundaries

# =============================================================================
# Visualize Spillover Effects
# =============================================================================

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=('Spatial Decay of OZ Effects', 'Spatial Distribution of OZ Effects'),
    horizontal_spacing=0.12
)

# 1. Spillover gradient
distances = ['Direct<br>(OZ)', 'Adjacent<br>(1st order)', 'Near<br>(2nd order)', 'Far<br>(3rd order)']
effects = [spatial_result.direct_effect, spatial_result.spillover_effect, 
          spatial_result.second_order_spillover, 0.1]
errors = [spatial_result.direct_se, spatial_result.spillover_se, 
         spatial_result.second_order_se, 0.3]

bar_colors = [COLORS[5], '#FFA07A', '#FFDAB9', '#F5F5F5']  # coral gradient

fig.add_trace(
    go.Bar(x=distances, y=effects, name='Effect',
           marker_color=bar_colors,
           marker_line_color='black', marker_line_width=1,
           error_y=dict(type='data', array=errors, visible=True),
           showlegend=False),
    row=1, col=1
)

# Add significance stars as annotations
for i, (e, err) in enumerate(zip(effects, errors)):
    if e > 2 * err:  # Roughly significant
        fig.add_annotation(x=distances[i], y=e + err + 0.5, text='***',
                          showarrow=False, font=dict(size=12), row=1, col=1)
    elif e > 1.5 * err:
        fig.add_annotation(x=distances[i], y=e + err + 0.5, text='*',
                          showarrow=False, font=dict(size=12), row=1, col=1)

fig.add_hline(y=0, line_color='black', line_width=0.5, row=1, col=1)

# 2. Spatial visualization (simulated map)
oz_mask = oz_data['designated_oz'] == 1

# Simulate distance to nearest OZ for non-OZ tracts
np.random.seed(42)
oz_data['oz_distance_effect'] = np.where(
    oz_mask,
    spatial_result.direct_effect,
    spatial_result.spillover_effect * np.exp(-np.random.uniform(0, 2, len(oz_data)))
)

# All tracts scatter
fig.add_trace(
    go.Scatter(
        x=oz_data['longitude'], y=oz_data['latitude'],
        mode='markers',
        marker=dict(
            size=8,
            color=oz_data['oz_distance_effect'],
            colorscale='Reds',
            opacity=0.7,
            line=dict(width=0.5, color='black'),
            colorbar=dict(title='Estimated<br>Effect (%)', x=1.02, len=0.9)
        ),
        name='Tracts',
        showlegend=False
    ),
    row=1, col=2
)

# Highlight designated OZ tracts with ring markers
oz_points = oz_data[oz_mask]
fig.add_trace(
    go.Scatter(
        x=oz_points['longitude'], y=oz_points['latitude'],
        mode='markers',
        marker=dict(size=12, color='rgba(0,0,0,0)', 
                   line=dict(width=2, color=COLORS[0])),
        name='OZ Boundary'
    ),
    row=1, col=2
)

# Update layout
fig.update_layout(
    title=dict(text='Pro Tier: Spatial Spillover Analysis',
               font=dict(size=14, weight='bold')),
    height=450,
    showlegend=True,
    legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1)
)

fig.update_xaxes(title_text='', row=1, col=1)
fig.update_yaxes(title_text='Home Value Effect (%)', row=1, col=1)
fig.update_xaxes(title_text='Longitude', row=1, col=2)
fig.update_yaxes(title_text='Latitude', row=1, col=2)

fig.show()

# =============================================================================
# ENTERPRISE TIER PREVIEW: Comprehensive Evaluation
# =============================================================================

print("="*70)
print("ENTERPRISE TIER: Comprehensive OZ Evaluation")
print("="*70)

print("""
OpportunityZoneEvaluator provides:

   Evaluation Components:
   ┌────────────────────────────────────────────────────────────────┐
   │  1. SELECTION ANALYSIS                                         │
   │     ├── Eligibility verification                               │
   │     ├── Governor selection model                               │
   │     └── Strategic bias detection                               │
   ├────────────────────────────────────────────────────────────────┤
   │  2. INVESTMENT TRACKING                                        │
   │     ├── QOF capital flow analysis                              │
   │     ├── Investment type breakdown (real estate vs business)   │
   │     └── Temporal investment patterns                           │
   ├────────────────────────────────────────────────────────────────┤
   │  3. IMPACT ESTIMATION                                          │
   │     ├── Multi-method robustness (DiD, SCM, RDD)               │
   │     ├── Spatial spillovers                                     │
   │     └── Dynamic treatment effects                              │
   ├────────────────────────────────────────────────────────────────┤
   │  4. DISPLACEMENT ANALYSIS                                      │
   │     ├── Rent affordability changes                             │
   │     ├── Demographic shifts                                     │
   │     └── Small business displacement                            │
   ├────────────────────────────────────────────────────────────────┤
   │  5. COST-BENEFIT ANALYSIS                                      │
   │     ├── Tax expenditure accounting                             │
   │     ├── Net community benefit                                  │
   │     └── ROI by zone type                                       │
   └────────────────────────────────────────────────────────────────┘

   Outputs:
   ├── Tract-level scorecards
   ├── State-level summary reports
   ├── Policy recommendation memos
   └── Interactive dashboards
""")

print("\nExample API (Enterprise tier):")
print("""
```python
from krl_enterprise import OpportunityZoneEvaluator

# Initialize evaluator
evaluator = OpportunityZoneEvaluator(
    state='CA',
    data_source='census_api',
    investment_data='qof_database'
)

# Run comprehensive evaluation
report = evaluator.evaluate(
    outcomes=['home_values', 'employment', 'business_formation'],
    methods=['did', 'scm', 'spatial_did'],
    spillover_rings=2,
    displacement_check=True
)

# Generate outputs
report.tract_scorecards()           # Individual tract reports
report.state_summary()              # State-level findings
report.policy_brief()               # Executive summary
report.export_dashboard('html')     # Interactive dashboard
```
""")

print("\nContact info@krlabs.dev for Enterprise tier access.")

======================================================================
ENTERPRISE TIER: Comprehensive OZ Evaluation
======================================================================

OpportunityZoneEvaluator provides:

   Evaluation Components:
   ┌────────────────────────────────────────────────────────────────┐
   │  1. SELECTION ANALYSIS                                         │
   │     ├── Eligibility verification                               │
   │     ├── Governor selection model                               │
   │     └── Strategic bias detection                               │
   ├────────────────────────────────────────────────────────────────┤
   │  2. INVESTMENT TRACKING                                        │
   │     ├── QOF capital flow analysis                              │
   │     ├── Investment type breakdown (real estate vs business)   │
   │     └── Temporal investment patterns                           │
   ├────────────────────────────────────────────────────────────────┤
   │  3. IMPACT ESTIMATION                                          │
   │     ├── Multi-method robustness (DiD, SCM, RDD)               │
   │     ├── Spatial spillovers                                     │
   │     └── Dynamic treatment effects                              │
   ├────────────────────────────────────────────────────────────────┤
   │  4. DISPLACEMENT ANALYSIS                                      │
   │     ├── Rent affordability changes                             │
   │     ├── Demographic shifts                                     │
   │     └── Small business displacement                            │
   ├────────────────────────────────────────────────────────────────┤
   │  5. COST-BENEFIT ANALYSIS                                      │
   │     ├── Tax expenditure accounting                             │
   │     ├── Net community benefit                                  │
   │     └── ROI by zone type                                       │
   └────────────────────────────────────────────────────────────────┘

   Outputs:
   ├── Tract-level scorecards
   ├── State-level summary reports
   ├── Policy recommendation memos
   └── Interactive dashboards


Example API (Enterprise tier):

```python
from krl_enterprise import OpportunityZoneEvaluator

# Initialize evaluator
evaluator = OpportunityZoneEvaluator(
    state='CA',
    data_source='census_api',
    investment_data='qof_database'
)

# Run comprehensive evaluation
report = evaluator.evaluate(
    outcomes=['home_values', 'employment', 'business_formation'],
    methods=['did', 'scm', 'spatial_did'],
    spillover_rings=2,
    displacement_check=True
)

# Generate outputs
report.tract_scorecards()           # Individual tract reports
report.state_summary()              # State-level findings
report.policy_brief()               # Executive summary
report.export_dashboard('html')     # Interactive dashboard
```


Contact info@krlabs.dev for Enterprise tier access.

# =============================================================================
# External Validity Analysis
# =============================================================================

print("="*70)
print("EXTERNAL VALIDITY: Generalizability Assessment")
print("="*70)

# 1. Geographic heterogeneity
print("\n1. GEOGRAPHIC HETEROGENEITY")
print("   Examining whether OZ effects vary by region/market type...")

# Simulate regional variation
np.random.seed(42)
regions = ['Northeast', 'Southeast', 'Midwest', 'Southwest', 'West']
regional_effects = {
    'Northeast': 0.12,   # Hot markets
    'Southeast': 0.08,
    'Midwest': 0.05,
    'Southwest': 0.09,
    'West': 0.14         # Very hot markets
}

print(f"\n   Regional Effect Heterogeneity:")
for region, effect in regional_effects.items():
    bar = "█" * int(effect * 50)
    print(f"   {region:<12} {effect*100:>5.1f}%  {bar}")

effect_range = max(regional_effects.values()) - min(regional_effects.values())
print(f"\n   Effect range: {effect_range*100:.1f}pp")
print(f"   Coefficient of variation: {np.std(list(regional_effects.values()))/np.mean(list(regional_effects.values()))*100:.1f}%")

if effect_range > 0.05:
    print(f"\n   ⚠️  SUBSTANTIAL regional heterogeneity detected")
    print(f"      National average may not apply to specific regions")

# 2. Market conditions sensitivity
print("\n2. MARKET CONDITIONS SENSITIVITY")
print("   How do effects vary by local housing market heat?")

market_conditions = {
    'Hot (>10% appreciation)': 0.15,
    'Moderate (5-10%)': 0.09,
    'Cool (0-5%)': 0.04,
    'Declining (<0%)': 0.01
}

for condition, effect in market_conditions.items():
    status = "✅" if effect > 0.05 else "⚠️"
    print(f"   {status} {condition:<25} Effect: {effect*100:+.1f}%")

print(f"\n   💡 Implication: OZ benefits are pro-cyclical")
print(f"      Policy may amplify existing market trends rather than")
print(f"      driving recovery in distressed markets")

# 3. Temporal sensitivity
print("\n3. TEMPORAL SENSITIVITY")
print("   Are effects stable over time or period-specific?")

years = list(range(2018, 2023))
annual_effects = [0.02, 0.05, 0.12, 0.08, 0.10]  # COVID spike in 2020

print(f"\n   Annual Effect Estimates:")
for year, effect in zip(years, annual_effects):
    note = " ← COVID housing boom" if year == 2020 else ""
    print(f"   {year}: {effect*100:+.1f}%{note}")

# Check if COVID period is an outlier
pre_covid = np.mean(annual_effects[:2])
covid_period = annual_effects[2]
post_covid = np.mean(annual_effects[3:])

print(f"\n   Period comparison:")
print(f"   Pre-COVID (2018-19): {pre_covid*100:.1f}%")
print(f"   COVID (2020): {covid_period*100:.1f}%")
print(f"   Post-COVID (2021-22): {post_covid*100:.1f}%")

if covid_period > 1.5 * pre_covid:
    print(f"\n   ⚠️  COVID period effects may not persist")
    print(f"      Consider excluding 2020 for baseline estimates")

# 4. External validity summary
print("\n" + "="*70)
print("EXTERNAL VALIDITY SUMMARY")
print("="*70)

print(f"""
   GENERALIZABILITY ASSESSMENT:
   
   ✅ LIKELY TO GENERALIZE:
      • Core mechanism: Tax incentive attracts capital
      • Selection effects: Governors choose "promising" tracts
      • Spillover patterns: Adjacent tract effects
   
   ⚠️  MAY NOT GENERALIZE:
      • Magnitude of effects (market-dependent)
      • Timing of effects (economic cycle dependent)
      • Distributional impacts (local policy context)
   
   ❌ UNLIKELY TO GENERALIZE:
      • COVID-period amplification
      • State-specific selection criteria
      • Local displacement patterns

   RECOMMENDATIONS FOR POLICY TRANSFER:
   
   1. Adjust effect sizes for local market conditions
   2. Consider complementary policies for cooler markets
   3. Monitor for displacement especially in hot markets
   4. Use local selection criteria benchmarks
   5. Plan for cyclical variation in program effectiveness
""")

======================================================================
EXTERNAL VALIDITY: Generalizability Assessment
======================================================================

1. GEOGRAPHIC HETEROGENEITY
   Examining whether OZ effects vary by region/market type...

   Regional Effect Heterogeneity:
   Northeast     12.0%  ██████
   Southeast      8.0%  ████
   Midwest        5.0%  ██
   Southwest      9.0%  ████
   West          14.0%  ███████

   Effect range: 9.0pp
   Coefficient of variation: 32.7%

   ⚠️  SUBSTANTIAL regional heterogeneity detected
      National average may not apply to specific regions

2. MARKET CONDITIONS SENSITIVITY
   How do effects vary by local housing market heat?
   ✅ Hot (>10% appreciation)   Effect: +15.0%
   ✅ Moderate (5-10%)          Effect: +9.0%
   ⚠️ Cool (0-5%)               Effect: +4.0%
   ⚠️ Declining (<0%)           Effect: +1.0%

   💡 Implication: OZ benefits are pro-cyclical
      Policy may amplify existing market trends rather than
      driving recovery in distressed markets

3. TEMPORAL SENSITIVITY
   Are effects stable over time or period-specific?

   Annual Effect Estimates:
   2018: +2.0%
   2019: +5.0%
   2020: +12.0% ← COVID housing boom
   2021: +8.0%
   2022: +10.0%

   Period comparison:
   Pre-COVID (2018-19): 3.5%
   COVID (2020): 12.0%
   Post-COVID (2021-22): 9.0%

   ⚠️  COVID period effects may not persist
      Consider excluding 2020 for baseline estimates

======================================================================
EXTERNAL VALIDITY SUMMARY
======================================================================

   GENERALIZABILITY ASSESSMENT:

   ✅ LIKELY TO GENERALIZE:
      • Core mechanism: Tax incentive attracts capital
      • Selection effects: Governors choose "promising" tracts
      • Spillover patterns: Adjacent tract effects

   ⚠️  MAY NOT GENERALIZE:
      • Magnitude of effects (market-dependent)
      • Timing of effects (economic cycle dependent)
      • Distributional impacts (local policy context)

   ❌ UNLIKELY TO GENERALIZE:
      • COVID-period amplification
      • State-specific selection criteria
      • Local displacement patterns

   RECOMMENDATIONS FOR POLICY TRANSFER:

   1. Adjust effect sizes for local market conditions
   2. Consider complementary policies for cooler markets
   3. Monitor for displacement especially in hot markets
   4. Use local selection criteria benchmarks
   5. Plan for cyclical variation in program effectiveness

# =============================================================================
# Executive Summary
# =============================================================================

print("="*70)
print("OPPORTUNITY ZONE EVALUATION: EXECUTIVE SUMMARY")
print("="*70)

print(f"""
ANALYSIS OVERVIEW:
   Total tracts analyzed: {len(oz_data)}
   Eligible tracts: {oz_data['eligible'].sum()} ({oz_data['eligible'].mean()*100:.0f}%)
   Designated OZ tracts: {oz_data['designated_oz'].sum()} ({oz_data['designated_oz'].mean()*100:.0f}%)
   Analysis period: 2016-2022

KEY FINDINGS:

   1. SELECTION PATTERNS
      Evidence of strategic selection for "upside potential"
      Designated tracts had:
        • Higher education levels
        • Better transit access
        • Lower vacancy rates
      Policy implication: Benefits may concentrate in less-distressed areas
   
   2. INVESTMENT FLOWS
      Average QOF investment in OZ: ${oz_tracts['qof_investment'].mean()/1000:.1f}M per tract
      Concentration: Top 20% of OZ tracts received majority of investment
   
   3. IMPACT ESTIMATES
      Home value DiD effect: {oz_tracts['home_value_pct_change'].mean() - non_oz_tracts['home_value_pct_change'].mean():+.1f}% (vs non-OZ eligible)
      Employment effect: {oz_tracts['employment_change'].mean() - non_oz_tracts['employment_change'].mean():+.2f}pp
      Spillover effects: ~{spatial_result.spillover_effect:.1f}% to adjacent tracts
   
   4. EQUITY CONSIDERATIONS
      Strategic selection may limit impact on most distressed communities
      Displacement risks in high-investment zones
      Need for community benefit agreements

POLICY RECOMMENDATIONS:

   1. TARGETING: Consider selection criteria revision to prioritize
      highest-need communities
   
   2. MONITORING: Implement displacement tracking and early warning
      systems in high-investment zones
   
   3. COMPLEMENTARY POLICIES: Pair OZ designation with workforce
      development and affordable housing requirements
   
   4. TRANSPARENCY: Require QOF investment reporting at tract level

KRL SUITE COMPONENTS USED:
   • [Community] TreatmentEffectEstimator, basic DiD
   • [Pro] SpatialDiD, spillover analysis, propensity weighting
   • [Enterprise] OpportunityZoneEvaluator, displacement analysis
""")

print("\n" + "="*70)
print("OZ evaluation tools: kr-labs.io/opportunity-zones")
print("="*70)

======================================================================
OPPORTUNITY ZONE EVALUATION: EXECUTIVE SUMMARY
======================================================================

ANALYSIS OVERVIEW:
   Total tracts analyzed: 67
   Eligible tracts: 32 (48%)
   Designated OZ tracts: 14 (21%)
   Analysis period: 2016-2022

KEY FINDINGS:

   1. SELECTION PATTERNS
      Evidence of strategic selection for "upside potential"
      Designated tracts had:
        • Higher education levels
        • Better transit access
        • Lower vacancy rates
      Policy implication: Benefits may concentrate in less-distressed areas

   2. INVESTMENT FLOWS
      Average QOF investment in OZ: $819.5M per tract
      Concentration: Top 20% of OZ tracts received majority of investment

   3. IMPACT ESTIMATES
      Home value DiD effect: +9.9% (vs non-OZ eligible)
      Employment effect: +0.21pp
      Spillover effects: ~2.1% to adjacent tracts

   4. EQUITY CONSIDERATIONS
      Strategic selection may limit impact on most distressed communities
      Displacement risks in high-investment zones
      Need for community benefit agreements

POLICY RECOMMENDATIONS:

   1. TARGETING: Consider selection criteria revision to prioritize
      highest-need communities

   2. MONITORING: Implement displacement tracking and early warning
      systems in high-investment zones

   3. COMPLEMENTARY POLICIES: Pair OZ designation with workforce
      development and affordable housing requirements

   4. TRANSPARENCY: Require QOF investment reporting at tract level

KRL SUITE COMPONENTS USED:
   • [Community] TreatmentEffectEstimator, basic DiD
   • [Pro] SpatialDiD, spillover analysis, propensity weighting
   • [Enterprise] OpportunityZoneEvaluator, displacement analysis


======================================================================
OZ evaluation tools: kr-labs.io/opportunity-zones
======================================================================

oz_effect_pct = np.where(oz_data['designated_oz'] == 1, 0.08, 0)  # 8% OZ premium
oz_data['home_value_2022'] = oz_data['home_value_2016'] * (1 + base_appreciation + oz_effect_pct + ...)

Method	Purpose	KRL Component
Difference-in-Differences	Estimate treatment effect	`TreatmentEffectEstimator`
Propensity Score Weighting	Balance treated/control	`sklearn` LogisticRegression
Spatial DiD	Capture spillovers	`SpatialDiD` (Pro tier)
Event Study	Validate parallel trends	Custom implementation

Component	Package	Tier	Description
`FREDFullConnector`	`krl-data-connectors`	🔵 Professional	Full FRED access (800k+ series)
`TreatmentEffectEstimator`	`krl-causal-policy-toolkit`	🟣 Enterprise	DiD, IPW, Doubly Robust estimation
`get_logger`	`krl-core`	🟢 Community	Logging utilities

Feature	Required Tier	Status
County economic data	Professional	Required
Causal inference (DiD)	Enterprise	Optional (graceful fallback)
Core utilities	Community	✅ Included

Tier	Price	Features	Subscribe
Community	Free	Basic connectors, core models	GitHub
Professional	$149/mo	Full FRED access (800k+ series), county data	Subscribe →
Enterprise	Custom	Causal Policy Toolkit, dedicated support	Contact Sales

Duration	Price	Best For	Get Access
1 Hour	$5	Quick analysis	Buy Pass →
24 Hours	$15	Day project	Buy Pass →
7 Days	$99	Extended trial	Buy Pass →

Question	Method	Outcome
1. Did OZ increase investment?	DiD	QOF fund flows
2. Did investment improve outcomes?	DiD with weighting	Unemployment rate
3. Did OZ displace residents?	DiD	Housing costs, poverty
4. Did benefits spill to neighbors?	Spatial DiD	Neighbor outcomes

	tract_id	county_name	longitude	latitude	unemployment_rate_2016	unemployment_rate_2022	eligible	selection_score	designation_prob	designated_oz	...	employment_change	home_value_2016	home_value_2022	investment_post_2018	qof_investment	poverty_rate_2016	median_income_2016	education_pct	transit_access	vacancy_rate
0	PA_001	Adams	-77.22	39.87	4.1	3.4	0	0.000000	0.000000	0	...	0.7	500000.0	589462.544492	3.246029e+04	0.000000	21.886953	46313.270657	28.237370	0.589734	7.465007
1	PA_003	Allegheny	-79.98	40.47	5.1	4.0	0	0.000000	0.000000	0	...	1.1	500000.0	608495.524105	4.188976e+05	0.000000	20.845681	51504.610993	16.035988	0.600129	4.592828
2	PA_005	Armstrong	-79.47	40.81	7.6	5.1	1	0.883721	0.441860	0	...	2.5	500000.0	599815.496251	5.774672e+05	0.000000	16.023865	44367.932466	25.690252	0.395985	11.087508
3	PA_007	Beaver	-80.35	40.68	6.1	5.1	1	0.709302	0.354651	0	...	1.0	500000.0	619182.420968	2.373512e+05	0.000000	17.809679	49178.175789	27.160670	0.738645	8.726993
4	PA_009	Bedford	-78.49	39.99	6.1	4.5	1	0.709302	0.354651	1	...	1.6	500000.0	611255.185563	1.062216e+06	531107.873163	22.764769	49193.615175	29.522945	0.330931	7.219424

Threat	Severity	Mitigation	Residual Concern
Non-parallel trends	HIGH	Pre-trend tests, matching	Divergence may emerge post-treatment for non-OZ reasons
Selection on unobservables	MODERATE	Rich controls, IPW	Governor selection criteria may be unobserved
Anticipation effects	LOW	Use 2016 as pre-period	Early mover investors
SUTVA violation	MODERATE	Spatial DiD model	Complex spillover patterns
Heterogeneous treatment timing	LOW	Single designation wave	QOF fund entry is staggered

Component	Status	Notes
Direct treatment effect	Identified (under parallel trends)	Effect of OZ designation on designated tracts
Spillover effects	Partially identified	Requires spatial model specification
Mechanisms	NOT identified	Cannot distinguish investment channel from other effects
General equilibrium	NOT identified	Aggregate effects on regional economy not captured

Question	Assessment for OZ Policy
Geographic Scope	Results from sample states may not generalize to all states
Time Period	2017-2022 includes unique conditions (COVID, low rates)
Market Conditions	Hot real estate markets may show different effects than cold markets
Governor Selection	State-specific selection criteria limit cross-state comparisons

Variable	Source	Causal Interpretation
Unemployment (2016, 2022)	✅ REAL	⚠️ Treatment simulated, so DiD effect is simulated
Home values	❌ SIMULATED	❌ No causal interpretation valid
Investment flows	❌ SIMULATED	❌ No causal interpretation valid
Covariates	❌ SIMULATED	⚠️ Propensity model is demonstration only

Threat	Concern	Evidence	Severity
Non-parallel trends	Treated/control may have diverged anyway	Check pre-trends	HIGH
Anticipation	Investors positioned before announcement	Use 2016 baseline	LOW
Spillovers	Treatment affects controls	Spatial DiD results	MODERATE
Attrition	Differential missing data	Check balanced panel	LOW

Concern	Limitation
Pennsylvania only	Effects may differ in other states
County-level	Tract-level effects may differ
2016-2022 only	Longer-term effects unknown
Unemployment focus	Other outcomes (investment, rents) not analyzed

Opportunity Zone Policy Evaluation¶

KASS Notebook | Applied Econometrics Series¶

Overview¶

Learning Objectives¶

Key Methods¶

Policy Context¶

Prerequisites¶

Estimated Time: 35-45 minutes¶

KRL Suite Components & Pricing¶

Tier Requirements¶

Upgrade Options¶

Rental Passes (Pay-As-You-Go)¶

⚠️ HYBRID DATA STRUCTURE NOTICE¶

Motivation¶

The Place-Based Policy Challenge¶

The Opportunity Zone Natural Experiment¶

Research Questions¶

Prior Literature¶

1. Environment Setup¶

2. Fetch Real County Economic Data from FRED¶

Identification Strategy¶

Causal Estimand¶

Difference-in-Differences Specification¶

Identifying Assumptions¶

Threats to Identification¶

Propensity Score Weighting¶

Spatial Spillover Specification¶

What We Identify vs. What We Assume¶

3. Selection Analysis: Were Zones Selected Fairly?¶

4. Impact Estimation: Difference-in-Differences (Community Tier)¶

Pro Tier: Spatial Spillover Analysis¶

Enterprise Tier: Comprehensive OZ Evaluation¶

5. Executive Summary¶

External Validity & Generalizability¶

Key Questions for Policy Transfer¶

⚠️ CRITICAL LIMITATIONS¶

1. County-Level Aggregation Bias¶

2. Pre-Programmed Home Value Effects¶

3. Real vs. Simulated Data Boundary¶

Limitations & Interpretation¶

What This Analysis DOES Show¶

What This Analysis DOES NOT Show¶

Threats to Validity¶

Appropriate Uses of This Analysis¶

Sensitivity Analysis Recommendations¶

Reproducibility Notes¶

References¶

Opportunity Zone Program Research¶

Difference-in-Differences Methodology¶

Spatial Econometrics¶

Data Sources¶

KRL Suite Documentation¶

Appendix: Methodology Notes¶

Identification Strategy¶

Assumptions¶

Data Sources¶