Сравнение методов

	Многопериодная оценка контрфактического воздействия	Динамический метод разностей-разностей
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000s–2010s	2021
Автор метода	Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians	Callaway & Sant'Anna; Sun & Abraham
Тип	Causal inference / quasi-experimental evaluation	Causal inference / quasi-experimental
Основополагающий источник	Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Другие названия	multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Связанные	4	4
Сводка	Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.	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.