مقایسهٔ روش‌ها

	ارزیابی اثرات علی علی‌رغم شرایط متقابل در دوره‌های متعدد	تفاوت در تفاوت‌های پویا
حوزه	استنتاج علّی	استنتاج علّی
خانواده	Regression model	Regression model
سال پیدایش	2000s–2010s	2021
پدیدآور	Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians	Callaway & Sant'Anna; Sun & Abraham
نوع	Causal inference / quasi-experimental evaluation	Causal inference / quasi-experimental
منبع بنیادین	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.
نام‌های دیگر	multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
مرتبط	4	4
خلاصه	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.