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| 이질적 처리 효과의 이중 강건 추정× | 역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2018-2023 | 2000 |
| 창시자≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | Robins, Hernán & Brumback |
| 유형≠ | Semiparametric causal inference | Causal inference weighting estimator |
| 원전≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 별칭≠ | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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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