  Important questions for impact evaluation require knowledge not only of
average effects, but of the distribution of treatment effects. What proportion
of people are harmed? Does a policy help many by a little? Or a few by a lot?
The inability to observe individual counterfactuals makes these empirical
questions challenging. I propose an approach to inference on points of the
distribution of treatment effects by incorporating predicted counterfactuals
through covariate adjustment. I show that finite-sample inference is valid
under weak assumptions, for example when data come from a Randomized Controlled
Trial (RCT), and that large-sample inference is asymptotically exact under
suitable conditions. Finally, I revisit five RCTs in microcredit where average
effects are not statistically significant and find evidence of both positive
and negative treatment effects in household income. On average across studies,
at least 13.6% of households benefited and 12.5% were negatively affected.
