区分变量与状态层面的因果效应可识别性，揭示新知识如何提升识别能力。
On the Granularity of Causal Effect Identifiability
- 从变量级拓展到状态级，研究干预对具体状态的影响
- 状态级效应可在变量级不可识别时仍可识别
- 结合上下文独立性等额外知识能提升识别效果
经典因果效应可识别性基于处理变量和结果变量定义。本文探讨状态级因果效应：对处理变量特定状态的干预如何影响结果变量的特定状态。我们证明，即使变量级因果效应不可识别，状态级效应仍可能可识别。这种分离仅在具备额外知识（如上下文特定独立性）时发生。我们进一步研究约束变量状态的知识，发现其与上下文独立性结合后，可同时提升变量级和状态级的可识别性。最后提出一种在附加约束下的因果效应识别方法，并通过实证研究展示两种识别层级间的差异。
原文摘要 · Abstract (English)
The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the identifiability of state-based causal effects: how an intervention on a particular state of treatment variables affects a particular state of outcome variables. We demonstrate that state-based causal effects may be identifiable even when variable-based causal effects may not. Moreover, we show that this separation occurs only when additional knowledge -- such as context-specific independencies -- is available. We further examine knowledge that constrains the states of variables, and show that such knowledge can improve both variable-based and state-based identifiability when combined with other knowledge such as context-specific independencies. We finally propose an approach for identifying causal effects under these additional constraints, and conduct empirical studies to further illustrate the separations between the two levels of identifiability.
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