Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge.

翻译：由于个人偏好和背景原因,个人在面对同一背景时往往会作出不同的决定。例如,法官对某些与毒品有关的罪行可能会有不同的宽大度,医生可能偏好如何开始治疗某些类型的病人。考虑到这些例子,我们提出一种算法,用以确定决策者之间意见高度分歧的背景类型(如案件类型或病人)。我们将此作为一个因果推论问题正式化,寻求一个决策者分配对决定具有重大因果效应的区域。我们的算法通过最大限度地实现经验目标而发现这样一个区域,我们给其性能设定了一个一般化的界限。在半合成实验中,我们表明我们的算法恢复了准确与基线比较的异质的正确区域。最后,我们将我们的算法应用于真实世界的保健数据集,恢复与现有临床知识相一致的变异性。