Comparer des méthodes

	Équilibrage par entropie	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012	1973
Auteur d'origine	Jens Hainmueller	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Covariate-balancing reweighting	Nonparametric matching / causal inference
Source fondatrice	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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.