مقایسهٔ روشها
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| Heterogeneous treatment effect Doubly robust estimation× | برآورد دوگانه استوار (AIPW)× | |
|---|---|---|
| حوزه | استنتاج علّی | استنتاج علّی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2018-2023 | 2005 |
| پدیدآور≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | Robins & Rotnitzky; Bang & Robins |
| نوع≠ | Semiparametric causal inference | Semiparametric causal estimator |
| منبع بنیادین≠ | Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049. 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 ↗ |
| نامهای دیگر | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| مرتبط | 5 | 5 |
| خلاصه≠ | Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates. | 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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