  We propose a method to reduce variance in treatment effect estimates in the
setting of high-dimensional data. In particular, we introduce an approach for
learning a metric to be used in matching treatment and control groups. The
metric reduces variance in treatment effect estimates by weighting covariates
related to the outcome and filtering out unrelated covariates.
