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| Wielookresowe ważenie odwrotnością prawdopodobieństwa× | Dynamiczne ważenie odwrotnością prawdopodobieństwa× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2000 | 1986-2000 |
| Twórca≠ | Robins, Hernan & Brumback | James M. Robins and colleagues |
| Typ≠ | Weighted causal estimator | Causal weighting estimator |
| Źródło pierwotne | 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., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Inne nazwy | longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW | Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies. | 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. |
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