Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Мультипериодная двукратно робастная оценка× | Двухробастное оценивание (AIPW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1994-2021 | 2005 |
| Автор метода≠ | Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021) | Robins & Rotnitzky; Bang & Robins |
| Тип | Semiparametric causal estimator | Semiparametric causal estimator |
| Основополагающий источник≠ | Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. 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 ↗ |
| Другие названия | longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point. | 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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