在存在隐藏因果路径时，仍能识别变量间因果方向。
Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
- 通过新回归集刻画残差独立性，突破隐藏变量限制
- 提出可完全识别因果方向的新条件，适用于多种隐藏路径场景
- 算法兼具理论完备性与实测竞争力，适合高复杂度因果推断
因果加法模型为存在隐藏变量时的因果发现提供了可处理且表达力强的框架。当两个变量间存在未观测到的后门路径或因果路径时，现有理论通常无法识别其因果关系。本文建立了在许多此类情况下可识别因果方向的充分条件。这些条件依赖于对回归集的新刻画，用于判断回归残差间的独立性以及可观测变量间的条件独立性。基于这些结果，我们提出了一个结合创新机制的搜索算法，并证明了其正确性和完备性。实验评估表明，该方法在性能上可与当前最先进方法相媲美。
原文摘要 · Abstract (English)
Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations of regression sets to determine independence among regression residuals and conditional independencies among observed variables. Building on these results, we introduce a search algorithm that incorporates these innovations and prove its soundness and completeness. Empirical evaluations demonstrate its competitive performance against state-of-the-art methods.
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