Step-by-step causal inference — method selection, assumptions, and robustness checks
-
Updated
Sep 23, 2026 - Python
Step-by-step causal inference — method selection, assumptions, and robustness checks
Estimate an AKM-style two-way fixed effects model using Canadian matched employer-employee data.
Estimation of Difference-in-Differences Treatment Effects with Staggered Treatment Onset Using Heterogeneity-Robust Two-Way Fixed Effects Regressions
TWFE plus Double Machine Learning on a synthetic 240k-row panel, with Optuna nuisance tuning under R-loss and DR-loss. Shows why the nonlinear basis must be built before entity demeaning.
To associate your repository with the two-way-fixed-effects topic, visit your repo's landing page and select "manage topics."