Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials

TL;DR

This paper introduces a regression adjustment method for estimating distributional treatment effects in randomized trials, improving precision and revealing nuanced treatment impacts across different contexts.

Contribution

It develops a novel distributional regression adjustment technique that enhances efficiency without restrictive assumptions, with practical inference and real-world applications.

Findings

Behavioral nudges shift water consumption from high to moderate levels.

Health insurance reduces zero doctor visits by 6.6 percentage points.

Method improves precision and detects effects missed by traditional approaches.

Abstract

In this paper, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment heterogeneity compared to average treatment effects. We propose a regression adjustment method that utilizes distributional regression and pre-treatment information, establishing theoretical efficiency gains without imposing restrictive distributional assumptions. We develop a practical inferential framework and demonstrate its advantages through extensive simulations. Analyzing water conservation policies, our method reveals that behavioral nudges systematically shift consumption from high to moderate levels. Examining health insurance coverage, we show the treatment reduces the probability of zero doctor visits by 6.6 percentage points while increasing the likelihood of 3-6 visits. In both applications, our regression adjustment method substantially improves precision and identifies treatment effects that were statistically insignificant under conventional approaches.