So sánh phương pháp

	Phương pháp Kiểm soát Tổng hợp Bayes	Phương pháp Kiểm soát Tổng hợp (SCM)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	2003–2010
Người khởi xướng	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Loại	Bayesian causal inference / synthetic control	Quasi-experimental causal inference
Công trình gốc	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.
Tên gọi khác	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Liên quan	5	4
Tóm tắt	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.