AI 中文总结
研究存在潜在变量和选择偏差时的局部因果结构学习，提出LoCaLS算法，刻画局部区域并建立理论桥梁，该算法在标准假设下合理完备，实验证明其结构精度高于局部方法且计算量小于全局方法，在生物数据分析中有实际应用。
AI 中文摘要
从观测数据中发现目标变量的直接因果关系是因果发现中的一个基本问题，在基因调控分析和生物医学研究等领域有广泛应用。现有因果发现方法要么学习全局因果结构，计算成本高，要么假设无潜在变量和选择偏差，而现实中常不满足。本文研究存在潜在变量和选择偏差时的局部因果结构学习。首先刻画能进行目标特定因果发现的局部区域，建立局部区域观测分布因果信息与全局因果结构相应信息的理论桥梁。在此基础上提出LoCaLS算法，在标准假设下合理且完备，能识别与全局因果发现方法相同的目标变量直接因果关系，允许潜在变量和选择偏差。实验表明该方法比现有局部方法结构精度更高，计算量比全局方法小得多，在实际基因表达数据集分析中有实际适用性。
英文摘要
Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research. Existing causal discovery methods either learn a global causal structure, which incurs substantial computational cost, or assume the absence of latent variables and selection bias, assumptions that are often violated in real-world settings. Motivated by these challenges, we study local causal structure learning in the presence of latent variables and selection bias. Specifically, we first characterize a local region that enables target-specific causal discovery without recovering the entire global structure. We then establish a theoretical bridge between causal information learned from the observed distribution induced on this local region and the corresponding information in the global causal structure. Building on these foundations, we propose LoCaLS, a local causal structure learning algorithm that is sound and complete under standard assumptions and identifies the same direct causes and effects of a target variable as those identifiable by global causal discovery methods, while allowing for latent variables and selection bias. Extensive experiments on random and real-world structures demonstrate that the proposed method consistently achieves higher structural accuracy than existing local methods while requiring substantially less computational effort than state-of-the-art global methods. Furthermore, applications to two real-world gene expression datasets reveal biologically plausible target-specific causal structures, demonstrating its practical applicability in large-scale biological data analysis.
