  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.
