Sammenlign metoder

	Bayesiansk samsvarsestimator	Bayesiansk Propensity Score Matching
Fagfelt	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Opprinnelsesår	1978–1998	2012
Opphavsperson	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Type	Bayesian causal inference / nonparametric matching	Bayesian causal inference / matching
Opprinnelig kilde	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.
Alias	Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Relaterte	6	6
Sammendrag	The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distribution rather than relying on asymptotic standard errors, yielding credible intervals that reflect both sampling variability and prior knowledge.	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.