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

TL;DR

This paper introduces a robust causal discovery method for real-world time series that leverages power-law spectral features, improving accuracy and resilience to noise in diverse applications like finance and neuroscience.

Contribution

The paper proposes a novel causal discovery approach based on power-law spectral features, enhancing robustness against noise and spurious inferences in real-world data.

Findings

Outperforms state-of-the-art methods on synthetic benchmarks.

Demonstrates robustness on real-world datasets with known causal structures.

Effectively captures genuine causal signals in noisy time series.

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.