Comparer des méthodes

	Estimateur bayésien par appariement	Équilibrage par entropie
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1978–1998	2012
Auteur d'origine	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)	Jens Hainmueller
Type	Bayesian causal inference / nonparametric matching	Covariate-balancing reweighting
Source fondatrice	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.	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 matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Apparentées	6	6
Résumé	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.	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.