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.

本研究针对个体连续治疗与结果之间的剂量-反应关系，提出了一种新方法，以解决现有方法在不确定性量化方面的不足。我们将因果剂量-反应问题框架化为协变量转移，通过加权符合预测生成预测区间，结果显示该方法在生成稳健的预测区间方面具有显著意义，能够有效支持个性化医疗决策。

连续治疗的剂量-反应模型的符合预测