Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Mitmeperioodiline pöörd-tõenäosuskaalutamine× | Paneelandmete pöördtõenäosuskaalumine× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta | 2000 | 2000 |
| Looja | Robins, Hernan & Brumback | Robins, Hernan & Brumback |
| Tüüp≠ | Weighted causal estimator | Reweighting / causal inference |
| Algallikas | 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 ↗ |
| Rööpnimetused | longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW |
| Seotud≠ | 6 | 5 |
| Kokkuvõte≠ | 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. | Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods. |
| ScholarGateAndmestik ↗ |
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