Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастное сопоставление по показателю склонности× | Двухробастное оценивание (AIPW)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2016 (robust variance correction); 1983 (PSM foundations) | 2005 |
| Автор метода≠ | Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations | Robins & Rotnitzky; Bang & Robins |
| Тип≠ | Quasi-experimental matching estimator with robust inference | Semiparametric causal estimator |
| Основополагающий источник≠ | Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807. 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 | robust PSM, PSM with robust variance, bias-corrected PSM, matching with robust inference | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching. | 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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