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

Bruno Fava

Papers from arXiv.org

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.

Date: 2024-07, Revised 2025-07
New Economics Papers: this item is included in nep-ecm and nep-exp
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