这篇论文把Transformer的自注意力倒过来用，专门抓多元时间序列里的间接因果，还带验证模块，实验效果比现有方法好。搞因果推断的值得看看。
本文提出一个基于Transformer自注意力机制的因果发现框架，用于处理多元时间序列数据。新引入的倒置因果自注意力模块（CSAM）通过翻转token并引入注意力稀疏性，突出潜在和间接因果关系，抑制伪相关。框架还包含全局因果算法与因果验证模块，前者提供整体因果影响度量，后者增强识别出的因果关系的可靠性。在线性和非线性数据集上的实验及消融研究表明，该框架优于现有方法。
Causal Discovery with Inverted Self-attention for Multivariate Time Series
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.