Causal Inference from Slowly Varying Nonstationary Processes

TL;DR

This paper introduces a novel causal inference method for nonstationary time series using a time-varying filter model and spectral estimates, enabling causal identification in complex, real-world data.

Contribution

It develops a new restricted SCM framework for nonstationary processes and proposes efficient spectral-based procedures for causal inference.

Findings

Effective causal inference demonstrated on synthetic datasets.

Method outperforms existing approaches on real-world data.

Handles high-order, non-smooth filters successfully.

Abstract

Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity. 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.