This vignette discusses the basics of using Difference-in-Differences (DiD) designs to identify and estimate the average effect of participating in a treatment with a particular focus on tools from the did package. The background article for it is Callaway and Sant’Anna (2021), “Difference-in-Differences with Multiple Time Periods”.

The did package allows for multiple periods and variation in treatment timing

The did package allows the parallel trends assumption to hold conditional on covariates.

Treatment effect estimates coming from the did package do not suffer from any of the drawbacks associated with two-way fixed effects regressions or event study regressions when there are multiple periods / variation in treatment timing.

The did package can deliver disaggregated group-time average treatment effects as well as event-study type estimates (treatment effects parameters corresponding to different lengths of exposure to the treatment) and overall treatment effect estimates.