用信息论特征识别时间序列中的因果关系，突破传统方法局限。
Causal Discovery in Multivariate Time Series through Mutual Information Featurization
- 将因果发现从统计检验转为模式识别，利用信息流不对称性建模
- 在高维非线性场景下显著优于现有方法，零样本泛化能力强
- 适合研究复杂系统因果结构的科研人员，如生物网络、金融时序
在复杂多变量时间序列中发现因果关系是基础科学挑战。传统方法常受限于线性假设，或因非线性动态导致条件独立性检验失效。本文提出新范式：从统计检验转向模式识别。我们假设因果关联会在系统时间图中形成持久且可学习的信息流不对称性，即使明显条件独立性被掩盖。提出TD2C框架，通过监督学习识别由信息论与统计描述符构成的丰富特征中复杂的因果信号。仅在多样化合成数据上训练，TD2C即展现出出色的零样本泛化能力，对未见过的动力学和真实基准表现优异。结果表明，该方法在高维与非线性场景中持续领先于现有方法，为复杂系统因果结构揭示提供稳健且可扩展的新工具。
原文摘要 · Abstract (English)
Discovering causal relationships in complex multivariate time series is a fundamental scientific challenge. Traditional methods often falter, either by relying on restrictive linear assumptions or on conditional independence tests that become uninformative in the presence of intricate, non-linear dynamics. This paper proposes a new paradigm, shifting from statistical testing to pattern recognition. We hypothesize that a causal link creates a persistent and learnable asymmetry in the flow of information through a system's temporal graph, even when clear conditional independencies are obscured. We introduce Temporal Dependency to Causality (TD2C), a supervised learning framework that operationalizes this hypothesis. TD2C learns to recognize these complex causal signatures from a rich set of information-theoretic and statistical descriptors. Trained exclusively on a diverse collection of synthetic time series, TD2C demonstrates remarkable zero-shot generalization to unseen dynamics and established, realistic benchmarks. Our results show that TD2C achieves state-of-the-art performance, consistently outperforming established methods, particularly in high-dimensional and non-linear settings. By reframing the discovery problem, our work provides a robust and scalable new tool for uncovering causal structures in complex systems.
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