Khipu Research Methods Experimental & Research Frameworks
| Status: Coming Soon | Expected: Q3 2026 |
Overview
This domain provides methodological infrastructure for rigorous causal inference—tools that help researchers design studies, estimate treatment effects, and communicate uncertainty in ways that meet the standards of evidence-based practice.
Technical Approach
Problems Addressed
- Selection bias — Observational data conflates treatment effects with pre-existing differences between groups
- Heterogeneous effects — Average treatment effects mask variation across subpopulations that matters for targeting
- Identification fragility — Causal claims depend on assumptions that are rarely tested systematically
- Reproducibility gaps — Analysis choices are often undocumented, making replication difficult
- Temporal confounding — Time-varying treatments and staggered adoption complicate standard panel methods
Methodological Foundations
Our frameworks implement peer-reviewed causal inference methods:
| Method | Application | Key References |
|---|---|---|
| Difference-in-Differences | Policy evaluation with parallel trends | Callaway & Sant’Anna (2021), staggered adoption extensions |
| Synthetic Control | Comparative case studies with data-driven weighting | Abadie et al. (2010, 2015) |
| Regression Discontinuity | Sharp and fuzzy designs at policy thresholds | Cattaneo et al. optimal bandwidth selection |
| Propensity Score Methods | Matching, IPW, and doubly robust estimation | Imbens & Rubin (2015), AIPW estimators |
| Instrumental Variables | 2SLS and weak instrument robust inference | Stock & Yogo tests, Anderson-Rubin confidence sets |
| Double Machine Learning | High-dimensional confounding with cross-fitting | Chernozhukov et al. (2018) |
| Bayesian Causal Inference | Prior incorporation and posterior uncertainty | Stan/PyMC implementations |
| Meta-Learners | CATE estimation (S-learner, T-learner, X-learner, R-learner) | Künzel et al. (2019) |
Analysis Outputs
- Treatment effect estimates — ATE, ATT, ATU with confidence intervals and sensitivity bounds
- Heterogeneity analysis — Conditional average treatment effects by subgroup
- Diagnostics — Balance tables, parallel trends tests, placebo checks, sensitivity plots
- Robustness matrices — Effect stability across specification choices
- Pre-analysis documentation — Registered analysis plans with versioning
Appropriate Applications
- Randomized controlled trial analysis
- Quasi-experimental policy evaluation
- Program impact assessment for funders
- Academic research requiring causal identification
- Evidence synthesis and meta-analysis
- What Works Clearinghouse and similar evidence standards
- Grant proposals requiring rigorous evaluation designs
Planned Capabilities
Research Design Tools
- Power analysis with heterogeneous treatment effects
- Randomization inference frameworks
- Pre-analysis plan templates with versioning
Causal Inference Pipelines
- Modular estimators (IPW, AIPW, doubly robust)
- Sensitivity analysis for unobserved confounding
- Heterogeneous effects discovery and subgroup analysis
Reproducibility Infrastructure
- Automated audit trails from raw data to results
- Version-controlled analysis pipelines
- Replication package generation
Development Status
| Component | Status |
|---|---|
| Estimator library | In Development |
| Design optimization | Design Phase |
| Reproducibility tools | Design Phase |
| Documentation | Not Started |
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Khipu Research Labs — info@krlabs.dev