Linganisha mbinu

	Ulinganifu wa Alama ya Kuhamasisha ya Kibayesiani	Usawazishaji wa Entropia
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2012	2012
Mwanzilishi	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Jens Hainmueller
Aina	Bayesian causal inference / matching	Covariate-balancing reweighting
Chanzo asilia	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
Majina mbadala	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Zinazohusiana	6	6
Muhtasari	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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.