Comparar métodos

	Diseño de Estudio de Eventos con Efectos de Tratamiento Heterogéneos	Diferencias en Diferencias Dinámicas
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2021	2021
Autor original	Sun & Abraham (2021); Callaway & Sant'Anna (2021)	Callaway & Sant'Anna; Sun & Abraham
Tipo	Quasi-experimental causal inference	Causal inference / quasi-experimental
Fuente seminal	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
Relacionados	3	4
Resumen	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.