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| Dinamijsko ponderisanje inverznim verovatnoćama× | Izračunavanje dvostruke robustnosti (AIPW)× | |
|---|---|---|
| Oblast | Kauzalno zaključivanje | Kauzalno zaključivanje |
| Porodica | Regression model | Regression model |
| Godina nastanka≠ | 1986-2000 | 2005 |
| Tvorac≠ | James M. Robins and colleagues | Robins & Rotnitzky; Bang & Robins |
| Tip≠ | Causal weighting estimator | Semiparametric causal estimator |
| Temeljni izvor≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. 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 ↗ |
| Drugi nazivi | Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Srodne≠ | 4 | 5 |
| Sažetak≠ | Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle. | 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. |
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