So sánh phương pháp

	Ghép Xu hướng Xuất hiện Bayes (Bayesian Propensity Score Matching)	Ước lượng Mạnh mẽ Kép (AIPW)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2012	2005
Người khởi xướng	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Robins & Rotnitzky; Bang & Robins
Loại	Bayesian causal inference / matching	Semiparametric causal estimator
Công trình gốc	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Tên gọi khác	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Liên quan	6	5
Tóm tắt	Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.