We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training samples to adjust for an identifiable covariate shift while protecting against worst-case conditional distribution shift bounded in an $f$-divergence ball. Based on ideas from conformal inference and distributionally robust learning, we present an algorithm that outputs (approximately) valid and efficient prediction intervals in the presence of distributional shifts. As a use case, we apply the framework to sensitivity analysis of individual treatment effects with hidden confounding. The proposed methods are evaluated in simulation studies and three real data applications, demonstrating superior robustness and efficiency compared with existing benchmarks.

翻译：我们引入了一种细粒度框架，用于在分布偏移下对预测模型进行不确定性量化。该框架区分了协变量分布偏移与结果（$Y$）和协变量（$X$）之间条件关系的偏移。我们提出对训练样本进行重新加权，以调整可识别的协变量偏移，同时防范以$f$-散度球为界的、最坏情况下的条件分布偏移。基于共形推断和分布鲁棒学习的思想，我们提出了一种算法，该算法能在存在分布偏移的情况下输出（近似）有效且高效的预测区间。作为应用案例，我们将该框架用于存在隐藏混杂因素时的个体处理效应敏感性分析。所提出的方法在模拟研究和三项真实数据应用中进行了评估，与现有基准方法相比，显示出卓越的鲁棒性和效率。