Comparer des méthodes

	Méthode de Contrôle Synthétique Dynamique	Évaluation d'Impact Contrefactuel (EIC)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	1970s–2000s
Auteur d'origine	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Heckman, Imbens, Rubin, and the program evaluation literature
Type	Comparative case study / counterfactual estimation	Causal inference / program evaluation
Source fondatrice	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.	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.
Alias	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Apparentées	5	5
Résumé	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.