{"ID":2893989,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12257","arxiv_id":"2507.12257","title":"Robust Causal Discovery in Real-World Time Series with Power-Laws","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.12257","url_pdf":"https://arxiv.org/pdf/2507.12257v3","authors":"[\"Matteo Tusoni\",\"Giuseppe Masi\",\"Andrea Coletta\",\"Aldo Glielmo\",\"Viviana Arrigoni\",\"Novella Bartolini\"]","published":"2025-07-16T14:02:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.data-an\",\"stat.ML\",\"stat.OT\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}