Self-Consistent Conformal Prediction
<|reference_start|>Self-Consistent Conformal Prediction: 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.<|reference_end|>
@article{van der laan2024self-calibrating,
    title={Self-Calibrating Conformal Prediction},
    author={Lars van der Laan and Ahmed M. Alaa},
    journal={arXiv preprint arXiv:2402.07307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07307},
    primaryClass={stat.ML cs.LG stat.ME}
}