  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.
