विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मैचिंग एस्टीमेटर (Matching Estimator)× | कोर्स्ड एग्जैक्ट मैचिंग (CEM)× | |
|---|---|---|
| क्षेत्र | कारणात्मक अनुमान | कारणात्मक अनुमान |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1973 | 2011-2012 |
| प्रवर्तक≠ | Rubin (1973); large-sample theory by Abadie & Imbens (2006) | Iacus, King, & Porro |
| प्रकार≠ | Nonparametric matching / causal inference | Matching / causal inference |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | nearest-neighbor matching, NNM, matching on covariates, covariate matching | CEM, coarsened matching, monotonic imbalance bounding matching |
| संबंधित | 6 | 6 |
| सारांश≠ | 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. | 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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