Conformal Prediction for Dose-Response Models with Continuous Treatments

• URL: http://arxiv.org/abs/2409.20412v1
• Date: Mon, 30 Sep 2024 15:40:54 GMT
• Title: Conformal Prediction for Dose-Response Models with Continuous Treatments
• Authors: Jarne Verhaeghe, Jef Jonkers, Sofie Van Hoecke,
• Abstract summary: We present a novel methodology for generating prediction intervals for dose-response models. Our method approximates local coverage for every treatment value by applying kernel functions as weights in weighted conformal prediction.
• Score: 0.23213238782019321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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