提出向量变量因果发现新方法，解决群体数据因果推断难题
Nonlinear Causal Discovery for Grouped Data
- 将非线性加性噪声模型拓展至向量形式，支持群体变量因果推断
- 设计两步法：先确定变量组的因果顺序，再选最优因果图
- 在真实产线数据上验证，适用于有部分因果知识的复杂系统
从观测数据中推断因果关系近年来受到广泛关注，但现有方法多局限于标量随机变量。在神经科学、心理学、社会科学及工业制造等重要领域，感兴趣的因果单元往往是变量组而非单一标量测量。受此驱动，本文将非线性加性噪声模型扩展至处理随机向量，提出一种两步式因果图学习方法：首先推断随机向量间的因果顺序，其次通过模型选择识别与该顺序一致的最佳图结构。针对向量情形，本文提出了高效且新颖的解决方案，并在模拟实验中展现出强劲性能。最后，将方法应用于具有部分因果顺序先验的真实装配线数据，验证其有效性。
原文摘要 · Abstract (English)
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step approach for causal graph learning: First, infer the causal order among random vectors. Second, perform model selection to identify the best graph consistent with this order. We introduce effective and novel solutions for both steps in the vector case, demonstrating strong performance in simulations. Finally, we apply our method to real-world assembly line data with partial knowledge of causal ordering among variable groups.
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