Comparer des méthodes

	Méthode de contrôle synthétique multi-périodes	Différence-en-différences multi-périodes (DiD échelonné)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010-2021	2021
Auteur d'origine	Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021)	Callaway & Sant'Anna; Goodman-Bacon
Type	Quasi-experimental causal inference	Causal inference / panel regression
Source fondatrice	Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	multi-period SCM, extended synthetic control, synthetic control with multiple treatment periods, staggered synthetic control	staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD
Apparentées	5	5
Résumé	The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatment window. It constructs a weighted combination of untreated units that reproduces the treated unit's pre-treatment trajectory, then uses that synthetic counterfactual across all post-treatment periods to estimate time-varying treatment effects.	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.