Marginal Structural Models and Causal Inference in Epidemiology

James M. Robins; Miguel A. Hernán; Babette Brumback · Epidemiology · 2000

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Author / responsible party

James M. Robins; Miguel A. Hernán; Babette Brumback

Publisher

Epidemiology

Year

2000

Language

English

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In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.

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APA 7

Robins, J. M, Hernán, M. A, & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. https://doi.org/10.1097/00001648-200009000-00011

MLA

Robins, James M, et al. "Marginal Structural Models and Causal Inference in Epidemiology." 2000. https://doi.org/10.1097/00001648-200009000-00011.

Chicago

Robins, James M, Miguel A. Hernán, and Babette Brumback. 2000. "Marginal Structural Models and Causal Inference in Epidemiology.". https://doi.org/10.1097/00001648-200009000-00011.

Harvard

Robins, J. M, Hernán, M. A. and Brumback, B. 2000, Marginal Structural Models and Causal Inference in Epidemiology, Epidemiology, available at: https://doi.org/10.1097/00001648-200009000-00011 [Accessed 7 Aug. 2026].

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Title: Marginal Structural Models and Causal Inference in Epidemiology

Author / contributors: James M. Robins; Miguel A. Hernán; Babette Brumback

Publisher: Epidemiology

Publication year: 2000

Language: English

Subjects

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Marginal structural model; Confounding; Causal inference; Observational study; Estimator; Inverse probability; Econometrics; Marginal model; Statistics; Inference; Causal model; Class (philosophy)