Comparer des méthodes

	Appariement exact à coarsinement pour les effets hétérogènes du traitement	Équilibrage par entropie
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2012-2013	2012
Auteur d'origine	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Jens Hainmueller
Type	Matching-based causal inference with subgroup CATE estimation	Covariate-balancing reweighting
Source fondatrice	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Apparentées	5	6
Résumé	Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much.	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.