Conformal Prediction for Dose-Response Models with Continuous Treatments
<|reference_start|>Conformal Prediction for Dose-Response Models with Continuous Treatments: Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting the need for uncertainty quantification to support informed decisions. Conformal prediction, a distribution-free and model-agnostic method for uncertainty quantification, has seen limited application in continuous treatments or dose-response models. To address this gap, we propose a novel methodology that frames the causal dose-response problem as a covariate shift, leveraging weighted conformal prediction. By incorporating propensity estimation, conformal predictive systems, and likelihood ratios, we present a practical solution for generating prediction intervals for dose-response models. Additionally, our method approximates local coverage for every treatment value by applying kernel functions as weights in weighted conformal prediction. Finally, we use a new synthetic benchmark dataset to demonstrate the significance of covariate shift assumptions in achieving robust prediction intervals for dose-response models.<|reference_end|>
@article{verhaeghe2024conformal,
    title={Conformal Prediction for Dose-Response Models with Continuous Treatments},
    author={Jarne Verhaeghe, Jef Jonkers, Sofie Van Hoecke},
    journal={arXiv preprint arXiv:2409.20412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20412},
    primaryClass={cs.LG cs.AI stat.ML}
}