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| Polityczna ocena z podwójnie odpornym estymatorem× | Estymacja podwójnie odporna (AIPW)× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1994-2005 | 2005 |
| Twórca≠ | Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005) | Robins & Rotnitzky; Bang & Robins |
| Typ | Semiparametric causal estimator | Semiparametric causal estimator |
| Źródło pierwotne≠ | Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. DOI ↗ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| Inne nazwy | DR estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
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