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arXiv · 2507.12257

Robust Causal Discovery in Real-World Time Series with Power-Laws

Matteo Tusoni·Giuseppe Masi·Andrea Coletta·Aldo Glielmo·Viviana Arrigoni·Novella Bartolini
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Abstract

Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world time series follow a power-law distribution, notably due to an inherent self-organizing behavior. Leveraging this insight, we build a robust CD method based on the extraction of power-law spectral features that amplify genuine causal signals. Our method consistently outperforms state-of-the-art alternatives on both synthetic benchmarks and real-world datasets with known causal structures, demonstrating its robustness and practical relevance.

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Matteo Tusoni, Giuseppe Masi, Andrea Coletta, Aldo Glielmo, Viviana Arrigoni, Novella Bartolini. 2026-06-03. Robust Causal Discovery in Real-World Time Series with Power-Laws. https://arxiv.org/abs/2507.12257

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