did-analysis

To estimate difference-in-differences effects, use a structured workflow requiring panel data with pre-treatment observations, clear treatment timing, and distinct treatment and control groups. This guides model selection from 2x2 DID to TWFE and robust alternatives to isolate causal effects.

Handle staggered adoption in difference-in-differences designs by using robust alternatives to standard TWFE, such as Callaway and Sant'Anna (CS/SA) or Sun-Abraham estimators. These methods prevent biased treatment effect estimates when units adopt policies at different times.

Validate parallel trends in difference-in-differences by running event-study diagnostics to visualize pre-treatment trends. This checks if treatment and control groups followed similar trajectories before the intervention, ensuring the core identifying assumption holds.

TWFE models in staggered adoption difference-in-differences setups can yield negative weights due to heterogeneous treatment effects across different timing cohorts. Using robust estimators like CS/SA or Sun-Abraham corrects this dynamic effect bias and provides accurate causal estimates.

Run robustness checks for difference-in-differences policy evaluations including placebo tests, alternative control groups, and sensitivity analyses. These steps validate your treatment timing and ensure that observed effects are not driven by unobserved time trends.

Yes, you can analyze heterogeneous treatment effects across subgroups in a difference-in-differences framework by performing subgroup analyses with robust standard errors. This allows you to identify varying causal impacts across different populations within your panel data.