Comparer des méthodes

	Évaluation de politiques par appariement exact à granularité grossière (CEM)	Équilibrage par entropie
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2011-2012	2012
Auteur d'origine	Iacus, King & Porro	Jens Hainmueller
Type	Matching / quasi-experimental design	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	CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Apparentées	5	6
Résumé	Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.	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.