Võrdle meetodeid

	Bayesilik kahekordne robustne hindamine	Bayesian Propensity Score Matching
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2005–2010s	2012
Looja	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Tüüp	Semiparametric causal estimation with Bayesian inference	Bayesian causal inference / matching
Algallikas	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.
Rööpnimetused	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Seotud	5	6
Kokkuvõte	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.