Methoden vergleichen

	Coarsened Exact Matching (CEM)	Matching-Schätzer
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2011-2012	1973
Urheber	Iacus, King, & Porro	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Typ	Matching / causal inference	Nonparametric matching / causal inference
Wegweisende Quelle	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.
Aliasnamen	CEM, coarsened matching, monotonic imbalance bounding matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Verwandt	6	6
Zusammenfassung	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.