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Research questionHow can causal discovery recover causal structures when time series shift across latent regimes?Methods that assume one stable causal graph can produce unreliable relationships when a time series moves between regimes. The challenge is to determine where stable causal structures hold and infer the appropriate structure for each interval.
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov BlanketsThe source presents a Markov-blanket-based algorithm for jointly discovering latent regimes and regime-specific causal graphs. Evidence is provided through theoretical recovery guarantees under stated assumptions, simulations with known ground truth, and real-world IT monitoring data; broader applicability beyond these settings is not established.research paper · Sep 4, 2026
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