Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Estimator de potrivire pentru evaluarea politicilor× | Potrivirea Exactă Coarsened (CEM)× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1998-2006 | 2011-2012 |
| Autorul original≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Iacus, King, & Porro |
| Tip≠ | Non-parametric causal estimator | Matching / causal inference |
| Sursa seminală≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. DOI ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| Denumiri alternative≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | CEM, coarsened matching, monotonic imbalance bounding matching |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. |
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