  A common concern in observational studies focuses on properly evaluating the
causal effect, which usually refers to the average treatment effect or the
average treatment effect on the treated. In this paper, we propose a data
preprocessing method, the Kernel-distance-based covariate balancing, for
observational studies with binary treatments. This proposed method yields a set
of unit weights for the treatment and control groups, respectively, such that
the reweighted covariate distributions can satisfy a set of pre-specified
balance conditions. This preprocessing methodology can effectively reduce
confounding bias of subsequent estimation of causal effects. We demonstrate the
implementation and performance of Kernel-distance-based covariate balancing
with Monte Carlo simulation experiments and a real data analysis.
