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Causal inference; confounding; counterfactual; double robust estimation; G-computation estimation; inverse probability of treatment weighted estimation
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Full-Text Articles in Statistical Models
Population Intervention Models In Causal Inference, Alan E. Hubbard, Mark J. Van Der Laan
Population Intervention Models In Causal Inference, Alan E. Hubbard, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a] treatment variable or risk variable on the distribution of a disease in a population. These models, as originally introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and its dependence on treatment. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at …