在因果图基础上引入逻辑约束，让原本无法识别的因果效应变得可识别。
Constrained Identifiability of Causal Effects
- 提出受限可识别性概念，将约束作为新输入纳入经典可识别性定义。
- 使用可计算的算术电路系统化测试受限可识别性，覆盖传统方法无法处理的情况。
- 实验证明：加入逻辑约束后，部分原不可识别的因果效应可被识别，适合因果推断研究者。
我们研究在存在不同类型约束（如逻辑约束）的情况下，因果效应的可识别性问题。这些约束对因果图所诱导的模型（参数化）施加限制，缩小了可识别性问题中考虑的模型集合。本文形式化了受限可识别性的概念，将一组约束作为经典可识别性定义的额外输入。随后提出一种基于可计算算术电路（ACs）的框架，用于系统测试受限可识别性。结果表明，该基于AC的方法至少与现有算法（如do-演算）一样完备，后者仅假设严格正性约束。通过实例说明，某些原本不可识别的因果效应在不同约束下可变为可识别。
原文摘要 · Abstract (English)
We study the identification of causal effects in the presence of different types of constraints (e.g., logical constraints) in addition to the causal graph. These constraints impose restrictions on the models (parameterizations) induced by the causal graph, reducing the set of models considered by the identifiability problem. We formalize the notion of constrained identifiability, which takes a set of constraints as another input to the classical definition of identifiability. We then introduce a framework for testing constrained identifiability by employing tractable Arithmetic Circuits (ACs), which enables us to accommodate constraints systematically. We show that this AC-based approach is at least as complete as existing algorithms (e.g., do-calculus) for testing classical identifiability, which only assumes the constraint of strict positivity. We use examples to demonstrate the effectiveness of this AC-based approach by showing that unidentifiable causal effects may become identifiable under different types of constraints.
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