Conditional predictive inference with $L^k$-coverage control

Abstract

We consider the problem of distribution-free conditional predictive inference. Prior work has established that achieving exact finite-sample control of conditional coverage without distributional assumptions is impossible, in the sense that it necessarily results in trivial prediction sets. While several lines of work have proposed methods targeting relaxed notions of conditional coverage guarantee, the inherent difficulty of the problem typically leads such methods to offer only approximate guarantees or yield less direct interpretations, even with the relaxations. In this work, we propose an inferential target as a relaxed version of conditional predictive inference that is achievable with exact distribution-free finite sample control, while also offering intuitive interpretations. One of the key ideas, though simple, is to view conditional coverage as a function rather than a scalar, and thereby aim to control its function norm. We propose a procedure that controls the $L^k$-norm -- while primarily focusing on the $L^2$-norm -- of a relaxed notion of conditional coverage, adapting to different approaches depending on the choice of hyperparameter (e.g., local-conditional coverage, smoothed conditional coverage, or conditional coverage for a perturbed sample). We illustrate the performance of our procedure as a tool for conditional predictive inference, through simulations and experiments on a real dataset.