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	Wielookresowe różnice w różnicach (staggered DiD)	Metoda syntetycznej kontroli (SCM)
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2021	2003–2010
Twórca	Callaway & Sant'Anna; Goodman-Bacon	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Typ	Causal inference / panel regression	Quasi-experimental causal inference
Źródło pierwotne	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Inne nazwy	staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Pokrewne	5	4
Podsumowanie	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.