用Transformer捕捉时间序列因果关系，还能排除虚假关联。
Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration
- 用多层Transformer建模变量间长期非线性依赖关系。
- 因果发现F1提升12.8%，因果时滞估计准确率达98.9%。
- 通过注意力掩码引入先验知识，有效抑制虚假因果关系。
我们提出一种新型时间因果发现与推断框架，解决复杂非线性依赖和虚假相关性两大挑战。方法采用多层Transformer时间序列预测器，捕捉变量间的长程非线性时间关系。训练完成后，利用基于梯度的分析从预测器中提取潜在因果结构及对应时滞，构建因果图。为减轻虚假因果关系的影响，引入基于注意力掩码的先验知识集成机制，在多个Transformer层中一致地强制排除用户指定的因果连接。大量实验表明，该方法显著优于现有最先进方法，在因果发现上实现12.8%的F1分数提升，因果时滞估计准确率达98.9%。
原文摘要 · Abstract (English)
We introduce a novel framework for temporal causal discovery and inference that addresses two key challenges: complex nonlinear dependencies and spurious correlations. Our approach employs a multi-layer Transformer-based time-series forecaster to capture long-range, nonlinear temporal relationships among variables. After training, we extract the underlying causal structure and associated time lags from the forecaster using gradient-based analysis, enabling the construction of a causal graph. To mitigate the impact of spurious causal relationships, we introduce a prior knowledge integration mechanism based on attention masking, which consistently enforces user-excluded causal links across multiple Transformer layers. Extensive experiments show that our method significantly outperforms other state-of-the-art approaches, achieving a 12.8% improvement in F1-score for causal discovery and 98.9% accuracy in estimating causal lags.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。