Сравнение методов

	Байесовская двойная робастная оценка	Байесовское сопоставление по показателю склонности
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2005–2010s	2012
Автор метода	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Тип	Semiparametric causal estimation with Bayesian inference	Bayesian causal inference / matching
Основополагающий источник	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.
Другие названия	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Связанные	5	6
Сводка	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.