Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
<|reference_start|>Identifying Nonstationary Causal Structures with High-Order Markov Switching Models: Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary time series with time-dependent effects or heterogeneous noise. In this work we address nonstationarity via regime-dependent causal structures. We first establish identifiability for high-order Markov Switching Models, which provide the foundations for identifiable regime-dependent causal discovery. Our empirical studies demonstrate the scalability of our proposed approach for high-order regime-dependent structure estimation, and we illustrate its applicability on brain activity data.<|reference_end|>
@article{balsells-rodas2024identifying,
    title={Identifying Nonstationary Causal Structures with High-Order Markov
  Switching Models},
    author={Carles Balsells-Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li},
    journal={arXiv preprint arXiv:2406.17698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17698},
    primaryClass={stat.ML cs.LG}
}