Work

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2406.17698

Authors: Balsells-Rodas, Carles, Wang, Yixin, Mediano, Pedro A. M., Li, Yingzhen

Institutions: Imperial College London, University of Michigan, University College London

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4400065248

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