D

difference-in-differences

Python research code that measures false-positive rates and minimum detectable lift of geo-experiment estimators by running placebo experiments on real county sales data with no intervention. Compares difference-in-differences, synthetic control, ridge-augmented SC, conformal tests and structural time series.

Reproducible causal inference and econometrics in Python: simulations, quasi-experiments, experiments, causal ML and marketing mix models, with explicit identification assumptions and diagnostics.

Python research code for synthetic control, donor-pool geometry, and placebo inference. Includes DiD, ridge-augmented SC, synthetic DiD, Monte Carlo experiments, and the California Proposition 99 case study.