  We propose a method for reporting how program evaluations reduce gaps between
groups, such as the gender or Black-white gap. We first show that the reduction
in disparities between groups can be written as the difference in conditional
average treatment effects (CATE) for each group. Then, using a
Kitagawa-Oaxaca-Blinder-style decomposition, we highlight how these CATE can be
decomposed into unexplained differences in CATE in other observables versus
differences in composition across other observables (e.g. the "endowment").
Finally, we apply this approach to study the impact of Medicare on American's
access to health insurance.
