学术文献库

Analysts often use data-driven approaches to supplement their substantive knowledge when selecting covariates for causal effect estimation. Multiple variable selection procedures tailored for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of data analysts. In this paper, we propose a Generalized Bayesian Causal Effect Estimation (GBCEE) algorithm to perform variable selection and produce double robust estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution that targets the selection of true confounders and predictors of the outcome for the unbiased estimation of causal effects with reduced standard errors. Double robust estimators provide some robustness against model misspecification, whereas the Bayesian machinery allows GBCEE to directly produce inferences for its estimate. GBCEE was compared to multiple alternatives in various simulation scenarios and was observed to perform similarly or to outperform double robust alternatives. Its ability to directly produce inferences is also an important advantage from a computational perspective. The method is finally illustrated for the estimation of the effect of meeting physical activity recommendations on the risk of hip or upper-leg fractures among elderly women in the Study of Osteoporotic Fractures. The 95% confidence interval produced by GBCEE is 61% shorter than that of a double robust estimator adjusting for all potential confounders in this illustration.