In this paper, we address the issue of estimating and inferring the distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment effects by characterizing heterogeneous effects across individual units, as opposed to relying solely on the average treatment effect. To enhance the precision of distributional treatment effect estimation, we propose a regression adjustment method that utilizes the distributional regression and pre-treatment information. Our method is designed to be free from restrictive distributional assumptions. We establish theoretical efficiency gains and develop a practical, statistically sound inferential framework. Through extensive simulation studies and empirical applications, we illustrate the substantial advantages of our method, equipping researchers with a powerful tool for capturing the full spectrum of treatment effects in experimental research.

翻译：本文针对随机实验中估计与推断分布处理效应的问题展开研究。相较于仅依赖平均处理效应，分布处理效应通过刻画个体单元间的异质性效应，提供了对处理效应更全面的理解。为提高分布处理效应估计的精度，我们提出一种回归调整方法，该方法利用分布回归与处理前信息。我们的方法设计上避免了严格的分布假设。我们建立了理论上的效率增益，并开发了一个实用且统计稳健的推断框架。通过广泛的模拟研究和实证应用，我们展示了该方法在捕捉实验研究中完整处理效应谱系方面的显著优势，为研究者提供了一个强有力的工具。