Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Novērtēšanas vienādošana (Matching Estimator)× | Coarsened Exact Matching (CEM)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1973 | 2011-2012 |
| Autors≠ | Rubin (1973); large-sample theory by Abadie & Imbens (2006) | Iacus, King, & Porro |
| Tips≠ | Nonparametric matching / causal inference | Matching / causal inference |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | nearest-neighbor matching, NNM, matching on covariates, covariate matching | CEM, coarsened matching, monotonic imbalance bounding matching |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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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