Predicting the Distribution of Treatment Effects via Covariate-Adjustment, with an Application to Microcredit

TL;DR

This paper introduces a new method for estimating the distribution of treatment effects using covariate adjustment, enabling impact evaluation beyond average effects, with applications to microcredit studies revealing heterogeneous impacts.

Contribution

It develops a novel inference approach for the distribution of treatment effects using predicted counterfactuals, with finite-sample and asymptotic validity, applied to microcredit impact analysis.

Findings

Distributional impacts vary significantly among individuals.

Some individuals benefit from increased credit access, others are harmed.

Null average effects mask important heterogeneity.

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 by incorporating 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.