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.

翻译：理解个体连续治疗与结果之间的剂量-响应关系能够极大推动决策制定，尤其在个性化药物剂量和个性化医疗干预等领域。在这些高风险场景中，点估计往往不足，凸显了不确定性量化对于支持知情决策的必要性。共形预测作为一种无需分布假设且与模型无关的不确定性量化方法，在连续治疗或剂量-响应模型中的应用尚不充分。为填补这一空白，我们提出一种创新方法，将因果剂量-响应问题构建为协变量偏移问题，并利用加权共形预测技术。通过整合倾向性估计、共形预测系统与似然比，我们提出了一种为剂量-响应模型生成预测区间的实用解决方案。此外，本方法通过将核函数作为加权共形预测中的权重，实现了对各治疗值的局部覆盖度的近似估计。最后，我们使用新型合成基准数据集，论证了协变量偏移假设对于获得稳健剂量-响应模型预测区间的重要性。