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| Entropy Balancing× | Estymator dopasowujący× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2012 | 1973 |
| Twórca≠ | Jens Hainmueller | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Typ≠ | Covariate-balancing reweighting | Nonparametric matching / causal inference |
| Źródło pierwotne≠ | Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| Inne nazwy | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step. | 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. |
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