Comparer des méthodes

	Appariement Bayésien par Score de Propension	Équilibrage par entropie
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012	2012
Auteur d'origine	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Jens Hainmueller
Type	Bayesian causal inference / matching	Covariate-balancing reweighting
Source fondatrice	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.
Alias	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Apparentées	6	6
Résumé	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.