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

• URL: http://arxiv.org/abs/2507.12257v1
• Date: Wed, 16 Jul 2025 14:02:21 GMT
• Title: Robust Causal Discovery in Real-World Time Series with Power-Laws
• Authors: Matteo Tusoni, Giuseppe Masi, Andrea Coletta, Aldo Glielmo, Viviana Arrigoni, Novella Bartolini,
• Abstract summary: Causal Discovery (CD) has been proposed to explore causal relationships in time series.<n>CD algorithms often exhibit a high sensitivity to noise, resulting in misleading causal inferences when applied to real data.<n>We build a robust CD method based on the extraction of power -law spectral features that amplify genuine causal signals.
• Score: 4.070469857434042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, but they often exhibit a high sensitivity to noise, resulting in misleading causal inferences when applied to real data. In this paper, we observe that the frequency spectra of typical 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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