विधियों की तुलना करें

	बायेसियन प्रोपेंसिटी स्कोर मैचिंग	प्रोपेंसिटी स्कोर मैचिंग
क्षेत्र	कारणात्मक अनुमान	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2012	1983
प्रवर्तक	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Paul Rosenbaum and Donald Rubin
प्रकार	Bayesian causal inference / matching	Method
मौलिक स्रोत	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
उपनाम	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	PSM, propensity score weighting, covariate balance
संबंधित	6	3
सारांश	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.