Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастный оценщик на основе подбора пар (с коррекцией смещения)× | Взвешивание по обратной вероятности лечения (IPW / IPTW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2006/2011 | 2000 |
| Автор метода≠ | Abadie & Imbens | Robins, Hernán & Brumback |
| Тип≠ | Causal inference / matching | Causal inference weighting estimator |
| Основополагающий источник≠ | Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Другие названия≠ | bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates. | 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. |
| ScholarGateНабор данных ↗ |
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