Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многопериодное взвешивание по показателю склонности× | Двухробастное оценивание (AIPW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2000 | 2005 |
| Автор метода≠ | Robins, Hernán, and Brumback (building on Robins' g-computation framework) | Robins & Rotnitzky; Bang & Robins |
| Тип≠ | Quasi-experimental causal inference | Semiparametric causal estimator |
| Основополагающий источник≠ | Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. link ↗ | 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 ↗ |
| Другие названия | longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Связанные | 5 | 5 |
| Сводка≠ | Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding. | 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. |
| ScholarGateНабор данных ↗ |
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