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

The paper presents RCBNB-MB, a causal discovery algorithm that relaxes the assumption of a single, time‑consistent causal structure in time series. It identifies latent causal regimes—subsets of time points where a stable causal graph holds—and iteratively segments the series to recover both regime transitions and the corresponding causal graphs using Markov blankets. The authors provide theoretical guarantees and demonstrate through simulations and real IT monitoring data that RCBNB-MB outperforms baseline methods in detecting regime changes and their causal structures.

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