Comparer des méthodes

	Conception d'étude d'événement pour les effets de traitement hétérogènes	Différence-en-différences dynamique
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2021	2021
Auteur d'origine	Sun & Abraham (2021); Callaway & Sant'Anna (2021)	Callaway & Sant'Anna; Sun & Abraham
Type	Quasi-experimental causal inference	Causal inference / quasi-experimental
Source fondatrice	Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	HTE event study, heterogeneous effects event study, group-time ATT event study, dynamic HTE design	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Apparentées	3	4
Résumé	Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approach explicitly models and recovers group-time average treatment effects (ATTs), addressing the contamination bias that arises when effects differ across treated units.	Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.