手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 二重に頑健な推定量（AIPW）× | 逆確率重み付け法 (IPW / IPTW)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2005 | 2000 |
| 提唱者≠ | Robins & Rotnitzky; Bang & Robins | Robins, Hernán & Brumback |
| 種類≠ | Semiparametric causal estimator | Causal inference weighting estimator |
| 原典≠ | 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 ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 別名≠ | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 関連 | 5 | 5 |
| 概要≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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