In machine learning and data mining, causal relationship discovery is a critical task. While the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR-LiNGAM) method excels in uncovering both instantaneous and time-lagged connections, it entails analyzing multiple VAR matrices, leading to heightened parameter complexity. To address this challenge, we introduce the Causal Graph Process-LiNGAM (CGP-LiNGAM), a novel approach that significantly reduces parameter load by focusing on a single causal graph, a Directed Acyclic Graph (DAG). Leveraging Graph Signal Processing (GSP) techniques, our method interprets causal relations with graph shift invariance and uniqueness. Our experimental results demonstrate the superiority and robustness of CGP-LiNGAM, particularly in high-noise environments. Moreover, we showcase its real-world applicability in studying brain connectivity during sleep, underlining its compatibility with previous sleep-related neuroscientific research.

在机器学习和数据挖掘中，因果关系发现是一项关键任务。虽然最先进的向量自回归线性非高斯非循环模型 (VAR-LiNGAM) 方法擅长发现瞬时和滞后连接，但它需要分析多个 VAR 矩阵，从而导致参数复杂性增加。为了应对这一挑战，我们引入了因果图处理-LiNGAM（CGP-LiNGAM），这是一种通过关注单个因果图（有向无环图（DAG））来显着减少参数负载的新颖方法。利用图信号处理（GSP）技术，我们的方法用图平移不变性和唯一性来解释因果关系。我们的实验结果证明了 CGP-LiNGAM 的优越性和鲁棒性，特别是在高噪声环境中。此外，我们展示了它在研究睡眠期间大脑连接的现实世界适用性，强调了它与之前与睡眠相关的神经科学研究的兼容性。