Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Paneeliaineiston marginaalinen rakenteellinen malli (MSM)× | Paneeliaineiston käänteisen todennäköisyyden painotus× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi | 2000 | 2000 |
| Kehittäjä≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins, Hernan & Brumback |
| Tyyppi≠ | Causal model for time-varying treatments | Reweighting / causal inference |
| Alkuperäislähde | 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 ↗ |
| Rinnakkaisnimet | MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle. | 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. |
| ScholarGateAineisto ↗ |
|
|