Comparer des méthodes

	Méthode robuste de contrôle synthétique	Méthode de contrôle synthétique bayésien
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2021	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)
Auteur d'origine	Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller
Type	Quasi-experimental causal inference	Bayesian causal inference / synthetic control
Source fondatrice	Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.	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.
Alias	Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC
Apparentées	5	5
Résumé	The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causal conclusions more defensible when only a single treated unit is observed.	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.