Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Heterogenní vážení inverzní pravděpodobností léčby (HTE-IPW)× | Dvojitě robustní odhad (AIPW)× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2003–2015 | 2005 |
| Tvůrce≠ | Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli | Robins & Rotnitzky; Bang & Robins |
| Typ≠ | Causal inference / weighted regression | Semiparametric causal estimator |
| Původní zdroj≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. 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 ↗ |
| Další názvy | HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics. | 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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