We explore a specific type of distribution shift called domain expertise, in which training is limited to a subset of all possible labels. This setting is common among specialized human experts, or specific focused studies. We show how the standard approach to distribution shift, which involves re-weighting data, can result in paradoxical disagreements among differing domain expertise. We also demonstrate how standard adjustments for causal inference lead to the same paradox. We prove that the characteristics of these paradoxes exactly mimic another set of paradoxes which arise among sets of voter preferences.

翻译：我们探讨了一种特定类型的分布偏移，称为领域专长，其中训练仅限于所有可能标签的一个子集。这种情形在专业人类专家或特定聚焦研究中普遍存在。我们展示了标准的分布偏移处理方法——即对数据进行重加权——如何导致不同领域专长之间出现悖论式的分歧。我们还证明了因果推断的标准调整会引发同样的悖论。我们证明这些悖论的特征恰好模拟了选民偏好集合中出现的另一组悖论。