Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
This work addresses nonstationary causal discovery for applications like neuroscience and climate science, representing an incremental advance by extending existing methods to high-order regimes.
The paper tackled the problem of causal discovery in nonstationary time series by proposing high-order Markov Switching Models to handle regime-dependent causal structures, establishing identifiability and demonstrating scalability with empirical studies on brain activity data.
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.