Comparer des méthodes

	Appariement par score de propension augmenté par l'apprentissage automatique	Équilibrage par entropie
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2004	2012
Auteur d'origine	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Jens Hainmueller
Type	Causal inference / matching	Covariate-balancing reweighting
Source fondatrice	McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.	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	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Apparentées	6	6
Résumé	Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).	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.