Conformal Prediction using Conditional Histograms
This addresses the need for reliable uncertainty quantification in regression tasks, particularly for skewed data, though it is incremental as it builds on existing conformal prediction methods.
The paper tackles the problem of computing prediction intervals for non-parametric regression that adapt to skewed data, resulting in intervals with marginal coverage in finite samples and improved performance in numerical experiments compared to state-of-the-art alternatives.
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.