Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo Estrutural Marginal (MSM)× | Estimativa Duplamente Robusta (AIPW)× | |
|---|---|---|
| Área | Inferência causal | Inferência causal |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2000 | 2005 |
| Autor original≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins & Rotnitzky; Bang & Robins |
| Tipo≠ | Causal model / semiparametric weighting | Semiparametric causal estimator |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | 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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