  This paper develops a conformal method to compute prediction intervals for
non-parametric regression that can automatically adapt to skewed data.
Leveraging black-box machine learning algorithms to estimate the conditional
distribution of the outcome using histograms, it translates their output into
the shortest prediction intervals with approximate conditional coverage. The
resulting prediction intervals provably have marginal coverage in finite
samples, while asymptotically achieving conditional coverage and optimal length
if the black-box model is consistent. Numerical experiments with simulated and
real data demonstrate improved performance compared to state-of-the-art
alternatives, including conformalized quantile regression and other
distributional conformal prediction approaches.
