Elements of Conformal Prediction for Statisticians

TL;DR

Conformal prediction offers a distribution-free, model-agnostic framework for predictive inference with finite-sample guarantees, suitable for high-dimensional and complex machine learning applications.

Contribution

This paper provides a clear, pedagogical overview of conformal prediction, explaining core ideas and selected methods without exhaustive survey, for statisticians.

Findings

Conformal prediction is distribution-free and model-agnostic.

It provides exact finite-sample guarantees under exchangeability.

The framework is well-suited for high-dimensional data and complex models.

Abstract

Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn from data. In recent years, conformal prediction has emerged as a rapidly growing alternative framework that is particularly well suited to modern applications involving high-dimensional data and complex machine learning models. Its appeal stems from being both distribution-free -- relying mainly on symmetry assumptions such as exchangeability -- and model-agnostic, treating the learning algorithm as a black box. Even under such limited assumptions, conformal prediction provides exact finite-sample guarantees, though these are typically of a marginal nature that requires careful interpretation. This paper explains the core ideas of conformal prediction and reviews selected methods. Rather than offering an exhaustive survey, it aims to provide a clear conceptual entry point and a pedagogical overview of the field.