Identifying Nonstationary Causal Structures with High-Order Markov Switching Models

TL;DR

This paper introduces a method for discovering causal structures in nonstationary time series by using high-order Markov switching models, enabling regime-dependent causal analysis in complex data like brain activity.

Contribution

It establishes identifiability for high-order Markov switching models and demonstrates scalable causal discovery in nonstationary time series.

Findings

Successful application to brain activity data

Scalable estimation of regime-dependent causal structures

Theoretical proof of model identifiability

Abstract

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.