did-causal

To estimate difference-in-differences effects with staggered adoption, use the Callaway-Sant'Anna estimator to calculate group-time average treatment effects ATT(g,t), which handles heterogeneous treatment effects across cohorts without the bias of classic methods.

TWFE DID estimators produce biased results under staggered rollout because heterogeneous treatment effects contaminate the weights applied to earlier and later treated groups. Use Goodman-Bacon decomposition to diagnose these bias patterns in your panel data.

To test for parallel trends before running DID analysis, estimate a Two-Way Fixed Effects event-study regression and plot the pre-treatment coefficients. Pre-trend placebo tests check if treated and control groups follow similar trends before treatment.

Yes, you can use Python linearmodels for event-study validation with unit and time fixed effects. It supports TWFE panel data regressions needed to diagnose pre-trends, while optional R packages handle Callaway-Sant'Anna and bacondecomp diagnostics.

The best way to diagnose TWFE bias in panel data econometrics is Goodman-Bacon decomposition. It breaks down the Two-Way Fixed Effects estimator into individual comparisons, revealing how staggered treatment timing and heterogeneous effects bias the overall estimate.

You need R packages like did and bacondecomp to compute Callaway-Sant'Anna ATT(g,t) estimates and run Goodman-Bacon decomposition. Python linearmodels handles TWFE and event-study regressions, but the C&S estimator requires the R ecosystem.