Comparer des méthodes

	Coarsened Exact Matching (CEM)	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2011-2012	1973
Auteur d'origine	Iacus, King, & Porro	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Matching / causal inference	Nonparametric matching / causal inference
Source fondatrice	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	CEM, coarsened matching, monotonic imbalance bounding matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.	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.