Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

2026-09-04

AI Summary

本文提出RCBNB-MB算法，通过识别时间序列中的潜在因果机制来解决非平稳时间序列的因果结构发现问题，利用马尔可夫毯提高鲁棒性。

Abstract

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.

Problem

• Time Series
• Causal Structures
• Latent Regimes
• Markov Blankets
• Regime Changes

Innovation

• Regime-aware Constraint-Based and Noise-Based causal discovery
• Markov Blankets
• latent causal regimes
• non-stationary time series