A Bayesian model averaging approach for estimating the parameters of longitudinal marginal structural models

Music City Center 

Numerous methods have been proposed in the last two decades for performing variable selection when the goal is to estimate the average treatment effect of a point exposure on an outcome. Much less work has focused on developing methods for performing variable selection when the goal is instead to estimate the causal effect of a longitudinal exposure on an outcome. We introduce a Bayesian model averaging approach to estimate the parameters of longitudinal marginal structural models. The Bayesian model averaging framework provides us with a principled way to produce valid post-selection inferences. We further devise an informative prior distribution that favors the selection of adjustment sets that produces estimators with reduced variance as compared with adjustment sets that include all potential confounders. We show through simulation results that our proposed method outperforms fully adjusted estimators and even performs similarly to an oracle estimator in several scenarios.

Causal inference

Bayesian model averaging

Longitudinal data

Variable selection

Model selection