  In observational studies, estimation of a causal effect of a treatment on an
outcome relies on proper adjustment for confounding. If the number of the
potential confounders ($p$) is larger than the number of observations ($n$),
then direct control for all potential confounders is infeasible. Existing
approaches for dimension reduction and penalization are generally aimed at
predicting the outcome, and are less suited for estimation of causal effects.
Under standard penalization approaches (e.g. Lasso), if a variable $X_j$ is
strongly associated with the treatment $T$ but weakly with the outcome $Y$, the
coefficient $\beta_j$ will be shrunk towards zero thus leading to confounding
bias.
  Under the assumption of a linear model for the outcome and sparsity, we
propose continuous spike and slab priors on the regression coefficients
$\beta_j$ corresponding to the potential confounders $X_j$. Specifically, we
introduce a prior distribution that does not heavily shrink to zero the
coefficients ($\beta_j$s) of the $X_j$s that are strongly associated with $T$
but weakly associated with $Y$. We compare our proposed approach to several
state of the art methods proposed in the literature. Our proposed approach has
the following features: 1) it reduces confounding bias in high dimensional
settings; 2) it shrinks towards zero coefficients of instrumental variables;
and 3) it achieves good coverages even in small sample sizes. We apply our
approach to the National Health and Nutrition Examination Survey (NHANES) data
to estimate the causal effects of persistent pesticide exposure on triglyceride
levels.
