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| نموذج الهيكل الهامشي لتقييم السياسات× | نموذج الهياكل الهامشية (MSM)× | |
|---|---|---|
| المجال | الاستدلال السببي | الاستدلال السببي |
| العائلة | Regression model | Regression model |
| سنة النشأة | 2000 | 2000 |
| صاحب الطريقة | James M. Robins, Miguel A. Hernan, Babette Brumback | James M. Robins, Miguel A. Hernan, Babette Brumback |
| النوع≠ | Causal inference / weighted regression | Causal model / semiparametric weighting |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة | MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | 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. | A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail. |
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