Marginal Structural Models and Causal Inference in Epidemiology
James M. Robins, Miguel A. Hernán, Babette Brumback
Abstract
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.
Topics & Concepts
Marginal structural modelConfoundingCausal inferenceObservational studyEstimatorInverse probabilityEconometricsMarginal modelStatisticsInferenceCausal modelClass (philosophy)MathematicsComputer scienceRegression analysisBayesian probabilityArtificial intelligencePosterior probabilityAdvanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods and Bayesian Inference