Causal Discovery in Multivariate Time Series through Mutual Information Featurization

• URL: http://arxiv.org/abs/2508.01848v1
• Date: Sun, 03 Aug 2025 17:03:13 GMT
• Title: Causal Discovery in Multivariate Time Series through Mutual Information Featurization
• Authors: Gian Marco Paldino, Gianluca Bontempi,
• Abstract summary: Temporal Dependency to Causality (TD2C) learns to recognize complex causal signatures from a rich set of information-theoretic and statistical descriptors.<n>Our results show that TD2C achieves state-of-the-art performance, consistently outperforming established methods.
• Score: 0.1657441317977376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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