Elements of Conformal Prediction for Statisticians

• URL: http://arxiv.org/abs/2603.23923v1
• Date: Wed, 25 Mar 2026 04:28:00 GMT
• Title: Elements of Conformal Prediction for Statisticians
• Abstract summary: Conformal prediction has emerged as a rapidly growing alternative framework for predictive inference.<n>This paper explains the core ideas of conformal prediction and reviews selected methods.
• Score: 17.012816639498023
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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