发表机构
Univ. Grenoble Alpes; CNRS; Grenoble INP; LIG; Sorbonne Université; INSERM; Institut Pierre Louis d’Epidémiologie et de Santé Publique（格勒诺布尔大学; 法国国家科学研究中心; 格勒诺布尔国立高等工程学院; 信息与信号处理实验室; 索邦大学; 法国国家健康与医学研究院; 皮埃尔·路易流行病学与公共卫生研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RCBNB-MB算法，突破时间序列平稳性假设，通过马尔可夫毯识别潜在因果状态与结构，经实验验证其在非平稳时间序列因果发现中优于基线方法。
AI 中文摘要
本文提出了RCBNB-MB（状态感知的基于约束与基于噪声的马尔可夫毯因果发现算法），这是一种针对时间序列的新型因果发现算法，它放宽了常见的单一、时间一致的因果结构假设。时间序列通常在离散时间点被观测，且常表现出状态变化，这对静态因果结构的假设构成了挑战，而静态因果结构假设是许多真实动态系统存在的局限性。为解决这一挑战，RCBNB-MB识别潜在因果状态，即时间点的子集，在该子集内稳定的因果结构成立。该算法遵循迭代策略，将时间序列分割为不同状态并在每个状态内发现因果图。通过利用马尔可夫毯而非直接父节点，RCBNB-MB对因果发现中的错误具有鲁棒性并保留预测信息。我们为RCBNB-MB在合理假设下恢复状态转换和因果图的能力提供了理论保证。此外，我们在具有已知真实值的模拟数据集和真实世界IT监控数据上进行了大量实验，验证了其有效性，在这些数据中考虑状态转换至关重要。实验结果表明，RCBNB-MB在准确检测状态变化及其相关因果图方面系统地优于基线方法，使其成为用于非平稳时间序列分析的鲁棒且通用的框架。
英文摘要
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxes the common assumption of a single, time-consistent causal structure. Time series are typically observed at discrete time points and often exhibit regime changes that challenge the assumption of a static causal structure, a limitation in many real-world dynamic systems. To address this challenge, RCBNB-MB identifies latent causal regimes, defined as subsets of time points within which a stable causal structure holds. The algorithm follows an iterative strategy that segments the time series into regimes and discovers the causal graph within each regime. By leveraging the Markov blanket rather than direct parents, RCBNB-MB gains robustness to errors in causal discovery and preserves predictive information. We provide theoretical guarantees for RCBNB-MB's ability to recover both regime transitions and causal graphs under reasonable assumptions. Furthermore, we validate its effectiveness through extensive experiments on simulated datasets with known ground truth and real-world IT monitoring data, where taking into account regime shifts is critical. Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.
CommentsAccepted at the 11th AALTD Workshop at ECML PKDD 2026, Naples, Italy
