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

arXiv 2507.12257 ↗First posted 16 Jul 2025cs.LG physics.data-an stat.MLcited by 0

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Abstract (arXiv)

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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References · 11

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  2. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
  3. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  4. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  5. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 36
  6. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  7. 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 23
  8. 2024Causal Discovery in Semi-Stationary Time Seriescited by 7
  9. 2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
  10. 2022Causal Discovery using Model Invariance through Knockoff Interventionscited by 4
  11. 2024Robust Time Series Causal Discovery for Agent-Based Model Validationcited by 3

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