  We propose a novel regression adjustment method designed for estimating
distributional treatment effect parameters in randomized experiments.
Randomized experiments have been extensively used to estimate treatment effects
in various scientific fields. However, to gain deeper insights, it is essential
to estimate distributional treatment effects rather than relying solely on
average effects. Our approach incorporates pre-treatment covariates into a
distributional regression framework, utilizing machine learning techniques to
improve the precision of distributional treatment effect estimators. The
proposed approach can be readily implemented with off-the-shelf machine
learning methods and remains valid as long as the nuisance components are
reasonably well estimated. Also, we establish the asymptotic properties of the
proposed estimator and present a uniformly valid inference method. Through
simulation results and real data analysis, we demonstrate the effectiveness of
integrating machine learning techniques in reducing the variance of
distributional treatment effect estimators in finite samples.
