无需非高斯假设，利用多视角数据实现因果发现
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
- 通过视角间相关性构建多视图线性结构方程模型
- 证明了无环结构下因果关系可识别，支持真实数据建模
- 适用于脑影像等多视角数据，适合研究复杂系统因果
因果发现通常依赖强假设，如非高斯分布。然而，现代许多应用提供同一系统的多个相关视角，这一特性在因果发现中尚未被充分挖掘。本文利用多视角结构，在弱假设下实现因果发现。提出一种多视图线性结构方程模型（multi-view linear SEM），通过视角间的相关性替代传统非高斯性假设。证明了该模型在无环结构下的可识别性。基于经典单视角算法（DirectLiNGAM、PairwiseLiNGAM、ICA-LiNGAM）设计多种多视图因果发现算法。通过仿真和脑影像数据分析验证，成功估计出脑区间的因果图。
原文摘要 · Abstract (English)
Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions. We propose a multi-view linear Structural Equation Model (SEM) that extends the well-known framework of non-Gaussian disturbances by alternatively leveraging correlation over views. We prove the identifiability of the model for acyclic SEMs. Subsequently, we propose several multi-view causal discovery algorithms, inspired by single-view algorithms (DirectLiNGAM, PairwiseLiNGAM, and ICA-LiNGAM). The new methods are validated through simulations and applications on neuroimaging data, where they enable the estimation of causal graphs between brain regions.
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