PICPIs: Prediction-Interval-Conditional Prediction Intervals
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal prediction in nonparametric uncertainty quantification, standard marginal validity offers limited resolution at the prediction values on which decisions are based, and fully conditional guarantees with respect to the covariates are provably unattainable. We address this gap by introducing a prediction-based conditioning framework that we refer to as Prediction-Interval-Conditional Prediction Intervals (PICPIs). Formally, a PICPI is an interval $I$ satisfying a self-consistency condition: $$\mathbb{E} [Y \mid p(X) \in I] \in I,$$ for predictive...
| Authors | Xuelin Yang, Baihe Huang, Yilong Hou et al. |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 13 October 2026 |
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