arXiv:2507.12257v4 Announce Type: replace 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 thi
The continuous research in AI, particularly in causal discovery, is driven by the increasing availability of complex real-world datasets across various domains and the need for more robust analytical tools.
Improving causal discovery in noisy real-world data enhances the reliability of AI applications across critical fields like finance, economics, and climate science, leading to more accurate predictions and interventions.
The ability to accurately identify causal relationships in power-law distributed time series significantly reduces spurious inferences, making AI systems more trustworthy and their insights more actionable.
- · AI researchers
- · Financial analysts
- · Climate scientists
- · Neuroscience research
- · Traditional statistical methods
- · AI models prone to noise sensitivity
More reliable AI models for forecasting and decision-making emerge in sectors dealing with complex time series.
Increased adoption of AI in previously noise-intolerant domains due to improved causal inference capabilities.
New economic models and policy frameworks are developed based on a deeper understanding of underlying causal dynamics in complex systems.
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