TY  - JOUR
TI  - Marginal Structural Models and Causal Inference in Epidemiology
AU  - James M. Robins
AU  - Miguel A. Hernán
AU  - Babette Brumback
PY  - 2000
JO  - Epidemiology
DO  - 10.1097/00001648-200009000-00011
UR  - https://doi.org/10.1097/00001648-200009000-00011
AB  - 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.
ER  -