标题： 具有幂律的现实时间序列中的稳健因果发现 显示英文标题

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

摘要： 探索随机时间序列中的因果关系是一个具有广泛应用的具有挑战性但至关重要的任务，包括金融、经济学、神经科学和气候科学。 许多用于因果发现（CD）的算法已经被提出，但它们通常对噪声非常敏感，导致在应用于真实数据时产生误导性的因果推断。 在本文中，我们观察到典型现实世界时间序列的频谱遵循幂律分布，这主要是由于内在的自组织行为。 利用这一见解，我们构建了一种基于提取放大真实因果信号的幂律频谱特征的稳健CD方法。 我们的方法在具有已知因果结构的合成基准和现实世界数据集上始终优于最先进的替代方法，证明了其稳健性和实际相关性。 显示英文摘要

摘要： 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.