مقایسهٔ روش‌ها

	تخمین بیزی با دوام مضاعف	تطابق امتیاز تمایل بیزی
حوزه	استنتاج علّی	استنتاج علّی
خانواده	Regression model	Regression model
سال پیدایش	2005–2010s	2012
پدیدآور	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
نوع	Semiparametric causal estimation with Bayesian inference	Bayesian causal inference / matching
منبع بنیادین	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.
نام‌های دیگر	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
مرتبط	5	6
خلاصه	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.