Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Dynamic Propensity Score Matching× | Двухробастное оценивание (AIPW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1986-2010 | 2005 |
| Автор метода≠ | Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching | Robins & Rotnitzky; Bang & Robins |
| Тип≠ | Sequential causal matching | Semiparametric causal estimator |
| Основополагающий источник≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. 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 | dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments. | 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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