Usporedite metode

	Policy Evaluation Matching Estimator	Grubljim točkastim podudaranjem (CEM)
Područje	Uzročno zaključivanje	Uzročno zaključivanje
Obitelj	Regression model	Regression model
Godina nastanka	1998-2006	2011-2012
Tvorac	Heckman, Ichimura & Todd; Abadie & Imbens	Iacus, King, & Porro
Vrsta	Non-parametric causal estimator	Matching / causal inference
Temeljni izvor	Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Drugi nazivi	matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator	CEM, coarsened matching, monotonic imbalance bounding matching
Srodne	6	6
Sažetak	The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.	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.