发表机构
College of Computer Science and Technology; Zhejiang University（计算机科学与技术学院; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出解耦因果发现（DCD），通过加权解耦识别马尔可夫边界并迭代构建CPDAG，无需传统假设，理论完备且在噪声环境下性能优异。
AI 中文摘要
从观测数据中进行因果发现是科学研究中一项基础且具有挑战性的任务。现有方法主要基于条件独立性检验、结构评分或限制性函数假设，而我们提出的解耦因果发现（DCD）提供了一种新颖的基于解耦的视角，不依赖这些方法。DCD通过加权函数对非目标变量进行解耦，直接识别马尔可夫边界（MB），使得在解耦分布下，仅MB内的变量与目标变量保持依赖关系。在此基础上，DCD利用MB内的结构不对称性，迭代构建完全部分有向无环图（CPDAG）。我们建立了DCD的理论可识别性、可靠性和完备性。实证评估表明，DCD取得了强劲的性能，尤其在具有挑战性的噪声环境中表现突出。
英文摘要
Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspective that does not rely on these methodologies. DCD directly identifies the Markov boundary (MB) by decoupling non-target variables via weighting functions, such that only variables within the MB preserve dependence with the target under the decoupled distribution. Building on this, DCD iteratively constructs the Completed Partially Directed Acyclic Graph (CPDAG) by exploiting structural asymmetries within the MBs. We establish the theoretical identifiability, soundness, and completeness of DCD. Empirical evaluations demonstrate that DCD achieves strong performance, particularly excelling in challenging noise regimes.
Comments24 pages
