发表机构
School of Computer Science, University of Technology Sydney; Technical University of Denmark; TianFu YongXing Laboratory; Faculty of Data Science, City University of Macau（悉尼科技大学计算机科学学院; 丹麦技术大学; 天府永兴实验室; 澳门城市大学数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多元时间序列因果发现的挑战，提出含倒置因果自注意力机制等模块的新型框架，经实验验证其性能优于现有方法。
AI 中文摘要
多元时间序列数据的因果发现因变量间复杂交互、高维度及非线性依赖而极具挑战性，现有方法往往难以捕捉这些复杂性，导致因果结构不准确。为解决该问题，我们提出一种利用Transformer架构内自注意力机制的新型因果发现框架，引入新颖的倒置因果自注意力机制（CSAM），通过倒置token并诱导注意力分数稀疏性，突出潜在及间接因果关系，聚焦显著因果交互并减少虚假相关性。此外，我们开发全局因果算法以识别全局因果链接，提供因果影响的整体度量，同时设置因果验证模块确保识别出的因果关系的稳健性，提升框架可靠性。在线性与非线性数据集上的实验，结合消融研究与敏感性分析表明，我们的框架优于现有方法，展现出在复杂多元时间序列因果发现中的应用潜力。
英文摘要
Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods often struggle to capture these complexities, resulting in inaccurate causal structures. To address this issue, we propose a novel framework that leverages self-attention mechanisms within the transformer architecture for causal discovery. Our approach introduces a novel inverted causal self-attention mechanism (CSAM) that emphasizes latent and indirect causal relationships by inverting tokens and inducing sparsity in attention scores, focusing on significant causal interactions and reducing spurious correlations. Additionally, we develop a global causal algorithm to identify global causal links, providing a holistic metric for causal influence, along with a causal verification module to ensure robustness in the identified causal relationships, enhancing the reliability of our framework. Experiments on both linear and nonlinear datasets, along with ablation studies and sensitivity analyses, show that our framework outperforms existing methods, demonstrating its potential for causal discovery in complex multivariate time series.
