Causal Inference from Slowly Varying Nonstationary Processes

2024/05/11 by Kang Du, Yu Xiang, Du, Kang +1
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Opinion Dynamics and Social Influence

paper · pdf · doi:10.48550/arxiv.2405.06902

openalex publication_date 2024/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this work, we propose a new class of restricted SCM, via a time-varying filter and stationary noise, and exploit the asymmetry from nonstationarity for causal identification in both bivariate and network settings. We propose efficient procedures by leveraging powerful estimates of the bivariate evolutionary spectra for slowly varying processes. Various synthetic and real datasets that involve high-order and non-smooth filters are evaluated to demonstrate the effectiveness of our proposed methodology.

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