Self-Consistent Conformal Prediction

• URL: http://arxiv.org/abs/2402.07307v2
• Date: Mon, 22 Apr 2024 04:15:10 GMT
• Title: Self-Consistent Conformal Prediction
• Authors: Lars van der Laan, Ahmed M. Alaa,
• Abstract summary: We introduce textitSelf-Consistent Conformal Prediction for regression. We provide calibrated point predictions and compatible prediction intervals that are valid conditional on model predictions.
• Score: 16.606421967131524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In decision-making guided by machine learning, decision-makers may take identical actions in contexts with identical predicted outcomes. Conformal prediction helps decision-makers quantify uncertainty in point predictions of outcomes, allowing for better risk management for actions. Motivated by this perspective, we introduce \textit{Self-Consistent Conformal Prediction} for regression, which combines two post-hoc approaches -- Venn-Abers calibration and conformal prediction -- to provide calibrated point predictions and compatible prediction intervals that are valid conditional on model predictions. Our procedure can be applied post-hoc to any black-box model to provide predictions and inferences with finite-sample prediction-conditional guarantees. Numerical experiments show our approach strikes a balance between interval efficiency and conditional validity.

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