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Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression.
Optimal probability weights for estimating causal effects of time-varying treatments with marginal structural cox models
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The central role of the propensity score in observational studies for causal effects
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Relation of pooled logistic regression to time dependent cox regression analysis: the framingham heart study
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Adjusting for nonignorable drop-out using semiparametric nonresponse models
Scharfstein, D. O., A. Rotnitzky, and J. M. Robins (1999) · 1999
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Marginal structural models to estimate the causal effect of zidovudine on the survival of hiv-positive men
Hernán, M. Á., B. Brumback, and J. M. Robins (2000) · 2000
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Marginal structural models versus structural nested models as tools for causal inference
Robins, J. M. (2000) · 2000
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Marginal structural models and causal inference in epidemiology
Robins, J. M., M. A. Hernan, and B. Brumback (2000) · 2000
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Marginal structural models to estimate the joint causal effect of nonrandomized treatments
Hernán, M. A., B. Brumback, and J. M. Robins (2001) · 2001
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Estimating the causal effect of zidovudine on cd4 count with a marginal structural model for repeated measures
Hernán, M. A., B. A. Brumback, and J. M. Robins (2002) · 2002
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Covariate balancing propensity score
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Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets
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