Comparer des méthodes

	Estimateur par appariement robuste (appariement corrigé du biais)	Coarsened Exact Matching (CEM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2006/2011	2011-2012
Auteur d'origine	Abadie & Imbens	Iacus, King, & Porro
Type	Causal inference / matching	Matching / causal inference
Source fondatrice	Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching	CEM, coarsened matching, monotonic imbalance bounding matching
Apparentées	6	6
Résumé	The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.	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.