staggered-adoption

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

Stata implementation of Double Difference-in-Differences (Egami & Yamauchi, 2023). Optimally combines standard DID and sequential DID via GMM for improved efficiency and robustness. Supports staggered adoption designs.

Synthetic DiD estimation in staggered adoption settings

The research estimates the effect on the dynamics of applications for unemployment benefits in response to the lifting of regional restrictive measures in the first wave of the COVID-19 pandemic in Russia

R package implementing Lee & Wooldridge (2025, 2026) rolling difference-in-differences estimator for panel data with staggered adoption, multiple estimators (RA/IPW/IPWRA/PSM), and exact small-sample inference.(Public Preview)(Disclaimer: CURRENTLY WIP)

Staggered-adoption causal platform: TWFE DiD, event study, parallel-trends and not-yet-treated ATTs + PSM in Python (statsmodels); TypeScript/Next.js dashboard with Zod validation; Jest + Playwright + pytest in Docker CI. Headline: GMV +6.3-8.2% (95% CI) on panel data.

Double Difference-in-Differences for Python — GMM-optimal combination of multiple pre-treatment periods with bootstrap inference, diagnostic tools, and publication-ready plotting (Egami & Yamauchi, 2023).