提出因果抽象的可识别性层级框架，解决未知完整因果图时的推理难题。
Identifiability in Causal Abstractions: A Hierarchy of Criteria
- 将因果抽象建模为一组因果图集合，定义可识别性标准
- 构建层级结构揭示不同知识水平下的可识别能力
- 适用于因果推断中部分已知或复杂系统的分析场景
从观察数据中识别处理效应通常需要完整的因果图假设，但在复杂或高维场景中此类图极少已知。为此，近期研究探索使用保留部分因果信息的因果抽象。本文将因果抽象形式化为因果图集合，关注其中因果查询的可识别性问题。我们引入并形式化若干可识别性标准，并将其组织成系统化的层级结构，揭示各标准间关系。该层级视角有助于厘清在不同因果知识水平下可识别的内容。通过文献中的例子说明框架应用，并提供在缺乏完整因果知识时进行可识别性推理的工具。
原文摘要 · Abstract (English)
Identifying the effect of a treatment from observational data typically requires assuming a fully specified causal diagram. However, such diagrams are rarely known in practice, especially in complex or high-dimensional settings. To overcome this limitation, recent works have explored the use of causal abstractions-simplified representations that retain partial causal information. In this paper, we consider causal abstractions formalized as collections of causal diagrams, and focus on the identifiability of causal queries within such collections. We introduce and formalize several identifiability criteria under this setting. Our main contribution is to organize these criteria into a structured hierarchy, highlighting their relationships. This hierarchical view enables a clearer understanding of what can be identified under varying levels of causal knowledge. We illustrate our framework through examples from the literature and provide tools to reason about identifiability when full causal knowledge is unavailable.
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