Robust Causal Discovery in Real-World Time Series with Power-Laws
This addresses the challenge of robust causal inference for applications like finance and neuroscience, though it appears incremental as an improvement over existing causal discovery methods.
The paper tackles the problem of causal discovery in noisy real-world time series by exploiting their power-law spectral distributions, resulting in a method that consistently outperforms state-of-the-art alternatives on synthetic and real datasets.
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.