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| Trọng số nghịch đảo xác suất hiệu ứng điều trị không đồng nhất (HTE-IPW)× | Ước lượng Mạnh mẽ Kép (AIPW)× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2003–2015 | 2005 |
| Người khởi xướng≠ | Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli | Robins & Rotnitzky; Bang & Robins |
| Loại≠ | Causal inference / weighted regression | Semiparametric causal estimator |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | 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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