Comparer des méthodes

	Méthode de contrôle synthétique bayésien	Méthode du Contrôle Synthétique (MCS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	2003–2010
Auteur d'origine	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Type	Bayesian causal inference / synthetic control	Quasi-experimental causal inference
Source fondatrice	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	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.
Alias	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Apparentées	5	4
Résumé	The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point.	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.