Сравнение методов

	Робастные оценки разности разностей (Robust Difference-in-Differences)	Многопериодный метод разностей разностей (последовательное DiD)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2021-2023	2021
Автор метода	Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023)	Callaway & Sant'Anna; Goodman-Bacon
Тип	Causal inference / panel regression	Causal inference / panel regression
Основополагающий источник	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Другие названия	robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD	staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD
Связанные	5	5
Сводка	Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way.	Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresses the bias that arises when conventional two-way fixed-effects regressions are applied to staggered adoption designs.