Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach

Abstract

Important questions for impact evaluation require knowledge not only of average effects, but of the distribution of treatment effects. The inability to observe individual counterfactuals makes answering these empirical questions challenging. I propose an inference approach for points of the distribution of treatment effects that uses predicted counterfactuals through covariate adjustment. I provide finite-sample valid inference using sample-splitting and asymptotically valid inference using cross-fitting under arguably weak conditions. Revisiting five randomized controlled trials on microcredit that reported null average effects, I find important distributional impacts, with some individuals helped and others harmed by the increased credit access.