手法を比較

	ベイズ的二重頑健推定量	ベイズ的傾向スコアマッチング
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2005–2010s	2012
提唱者	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
種類	Semiparametric causal estimation with Bayesian inference	Bayesian causal inference / matching
原典	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.
別名	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
関連	5	6
概要	Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.	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.