We explore what we call ``omitted label contexts,'' in which training data is limited to a subset of the possible labels. This setting is common among specialized human experts or specific focused studies. We lean on well-studied paradoxes (Simpson's and Condorcet) to illustrate the more general difficulties of causal inference in omitted label contexts. Contrary to the fundamental principles on which much of causal inference is built, we show that ``correct'' adjustments sometimes require non-exchangeable treatment and control groups. These pitfalls lead us to the study networks of conclusions drawn from different contexts and the structures the form, proving an interesting connection between these networks and social choice theory.

翻译：本文探讨了所谓的"标签缺失情境"，即训练数据仅包含可能标签的一个子集。这种情境常见于专业人类专家或特定聚焦研究中。我们借助深入研究的悖论（辛普森悖论和孔多塞悖论）来说明标签缺失情境下因果推断面临的普遍性困难。与因果推断所依据的基本原理相反，我们证明了"正确"的调整有时需要非可交换的处理组和对照组。这些陷阱引导我们研究从不同情境得出的结论网络及其形成的结构，证明了这些网络与社会选择理论之间存在有趣关联。