Conformal Prediction for Dose-Response Models with Continuous Treatments
This addresses the need for reliable uncertainty quantification in personalized healthcare interventions, such as drug dosing, which is incremental by extending conformal prediction to continuous treatments.
The paper tackled the problem of quantifying uncertainty in dose-response models for continuous treatments, proposing a novel methodology that uses weighted conformal prediction to generate prediction intervals, and demonstrated its significance on a synthetic benchmark dataset.
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.