Causality in extremes of time series

TL;DR

This paper introduces a novel method to detect causal relationships during extreme events in time series data, addressing limitations of traditional causal discovery methods in such contexts.

Contribution

The paper proposes the causal tail coefficient, a new tool for identifying asymmetric causal relations in extremes of time series, capable of handling nonlinearities and latent variables.

Findings

Effective in identifying causal relations during extreme events.

Performs well with large sample sizes in simulations.

Successfully applied to space-weather and hydro-meteorological data.

Abstract

Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a change in one causes a change in the other. Usual methods for causal discovery are not well suited if the causal mechanisms only appear during extreme events. We propose a framework to detect a causal structure from the extremes of time series, providing a new tool to extract causal information from extreme events. We introduce the causal tail coefficient for time series, which can identify asymmetrical causal relations between extreme events under certain assumptions. This method can handle nonlinear relations and latent variables. Moreover, we mention how our method can help estimate a typical time difference between extreme events. Our methodology is especially well suited for large sample sizes, and we show the performance on the simulations. Finally, we apply our method to real-world space-weather and hydro-meteorological datasets.