مقایسهٔ روشها
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| برآورد دورگه استوار (Doubly Robust Estimation) برای ارزیابی سیاست× | وزندهی احتمال معکوسِ دریافتِ درمان (IPW / IPTW)× | |
|---|---|---|
| حوزه | استنتاج علّی | استنتاج علّی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1994-2005 | 2000 |
| پدیدآور≠ | Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005) | Robins, Hernán & Brumback |
| نوع≠ | Semiparametric causal estimator | Causal inference weighting estimator |
| منبع بنیادین≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| نامهای دیگر≠ | DR estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| مرتبط | 5 | 5 |
| خلاصه≠ | Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation. | 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. |
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