KASS: Knowledge & Analytics for Social Science
Production-grade causal inference notebooks for policy analysis.
What This Is
KASS is a curated set of Jupyter notebooks that tackle real policy questions using rigorous causal inference methods. These aren’t toy examples—they’re the kind of analyses that federal agencies, research institutions, and policy shops actually run.
- A showcase of what’s possible with modern econometric methods
- A learning resource for applied researchers who need to estimate causal effects
- A technical proof-of-concept for the KRL (Khipu Research Labs) platform
Interactive Demos
View the fully rendered notebooks with all outputs, visualizations, and results:
| Notebook | Method | View |
|---|---|---|
| Heterogeneous Treatment Effects | Causal Forests, Double ML | View Demo → |
| Synthetic Control Policy Lab | Synthetic Control Method | View Demo → |
| Regression Discontinuity Toolkit | RDD, Bandwidth Selection | View Demo → |
| Labor Market Intelligence | BLS/Census Integration | View Demo → |
| Opportunity Zone Evaluation | DiD, Synthetic Controls | View Demo → |
| Workforce Development ROI | Cost-Benefit Analysis | View Demo → |
Why This Exists
Policy analysis has a credibility problem. Too much of what passes for “evaluation” relies on correlations dressed up as causation. Too many decisions get made on gut feeling because rigorous analysis takes too long.
We built KASS to demonstrate what changes when you have:
- Proper causal inference methodologies baked into your workflow
- Direct access to authoritative data sources (Census, BLS, administrative records)
- Reproducible pipelines that anyone can audit and extend
Quick Start
git clone https://github.com/KhipuResearch/KASS.git
cd KASS
pip install -r requirements.txt
jupyter lab