did-event-study

DiD event study analysis tracks treatment effects over time by comparing outcomes between treated and untreated groups. This Skill strictly follows the Sant'Anna methodology, applying doubly robust estimators to panel data to ensure robust causal inference and diagnostic validation.

The best way to perform an event study with doubly robust estimators is to use the Sant'Anna standard workflow. This Skill integrates the did and DRDID packages to apply these estimators, providing built-in diagnostics and sensitivity analysis for your panel data.

Yes, you need specific R packages like did, DRDID, didFF, fixest, and Stata packages like csdid and drdid. These dependencies provide the necessary functions for doubly robust estimation, staggered timing handling, and sensitivity analysis in your DiD workflow.

Yes, you can use the Sant'Anna DiD methodology for panel data with multiple treatment periods. This Skill is designed to handle staggered adoption, calculating group-time average treatment effects using doubly robust estimators to avoid the biases found in traditional two-way fixed effects models.

Yes, this DiD and event study workflow includes comprehensive diagnostics and sensitivity analysis. Adhering to the Sant'Anna standard, it validates the parallel trends assumption and tests the robustness of your treatment effect estimates to ensure methodological rigor.

Staggered difference-in-differences models show biased treatment effects when traditional two-way fixed effects models are used. This Skill resolves the issue by applying the Sant'Anna methodology, using doubly robust estimators to calculate accurate group-time average treatment effects.