通过机制变化识别隐藏混杂与选择偏差，提升因果推断准确性。
Identifying Structural Biases from Causal Mechanism Shifts
- 基于机制在不同环境中的变化模式，判断变量是否受偏倚影响。
- 提出可实证检验的互信息准则，准确识别偏倚类型与受影响变量。
- 算法StruBI在合成与真实数据上表现优于现有方法，适合因果建模研究者使用。
因果发现方法通常假设数据独立同分布且无未观测变量干扰，但实际中这些假设常被违反，导致推断错误。本文研究如何从因果机制变化中识别隐藏混杂和选择偏差。我们发现结构偏倚会导致机制变化之间的依赖性：通过分析不同环境下哪些变量的机制发生变化，可判断变量是否无偏、受隐藏混杂影响或存在选择偏差。为此，我们提出基于互信息的可实证检验准则，并明确了其适用条件。进一步设计了StruBI算法，用于判定具体节点的偏倚类型。在合成数据与真实世界数据上的实验表明，StruBI能精准恢复受影响变量集合及偏倚类型，显著优于当前最优方法。
原文摘要 · Abstract (English)
Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In practice, these assumptions are often violated, leading to inaccurate inference. In this paper, we study how to identify hidden confounding and selection biases from causal mechanism shifts. In particular, we show that structural biases lead to dependent mechanism shifts. That is, by considering for which variables the mechanisms change given data from different environments, we can tell which variables are unbiased, which are subject to hidden confounding, and which are undergoing selection bias. We formalize this into an empirically testable criterion based on mutual information, and show under which conditions it identifies structural biases. To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm. Experiments on synthetic and real-world data show that StruBI works well in practice, accurately recovering affected variable sets and types of biases, outperforming the state-of-the-art by a wide margin.
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