Сравнение методов

	Байесовское взвешивание по обратной вероятности	Байесовское сопоставление по показателю склонности
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2015	2012
Автор метода	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Тип	Bayesian causal weighting estimator	Bayesian causal inference / matching
Основополагающий источник	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	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 IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Связанные	6	6
Сводка	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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.