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Life sciences · Preprint
arXiv · September 4, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint describing RCBNB-MB, a novel algorithmic approach to causal discovery in non-stationary time series that relaxes the assumption of a single static causal structure. The work provides theoretical guarantees and reports superior performance versus baselines on simulated and IT monitoring data, but has not undergone peer review and addresses a computational problem rather than a clinical or biomedical outcome.
Preprint. Intervention: Regime-aware Constraint-Based and Noise-Based causal discovery algorithm with Markov Blankets (RCBNB-MB). Compared with: Baseline causal discovery approaches (unspecified).
Algorithm systematically outperforms baseline approaches in detecting regime changes and associated causal graphs Provides theoretical guarantees for recovery of both regime transitions and causal graphs under stated assumptions Validated on simulated datasets with known ground truth and real-world IT monitoring data
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This is an unrefereed preprint describing a novel computational algorithm with theoretical guarantees and validation on simulated and real-world data, but lacks clinical or patient outcomes and has not undergone peer review.
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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.
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