Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Politiikan arvioinnin marginaalinen rakenteellinen malli× | Kaksoisrobustin estimoinnin (AIPW) menetelmä× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2000 | 2005 |
| Kehittäjä≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins & Rotnitzky; Bang & Robins |
| Tyyppi≠ | Causal inference / weighted regression | Semiparametric causal estimator |
| 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. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| Rinnakkaisnimet | MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data. | 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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