The paper considers the problem of multi-objective decision support when outcomes are uncertain. We extend the concept of Pareto-efficient decisions to take into account the uncertainty of decision outcomes across varying contexts. This enables quantifying trade-offs between decisions in terms of tail outcomes that are relevant in safety-critical applications. We propose a method for learning efficient decisions with statistical confidence, building on results from the conformal prediction literature. The method adapts to weak or nonexistent context covariate overlap and its statistical guarantees are evaluated using both synthetic and real data.

翻译：文件考虑了在结果不确定时提供多目标决策支持的问题。我们扩展了Pareto高效决策的概念,以考虑到不同情况下决策结果的不确定性。这样可以量化在与安全关键应用相关的尾端结果决策之间的权衡。我们提出了一个以统计信心学习高效决策的方法,以一致预测文献的结果为基础。该方法适应了薄弱或不存在的环境共差重叠,其统计保障使用合成数据和真实数据进行评估。