On causality of extreme events

TL;DR

This paper introduces a novel causality metric for static data that detects non-linear causal relationships by analyzing extreme events, outperforming classical methods especially with large datasets and non-linearities.

Contribution

The paper proposes a new causality metric for static data sets based on extreme events, capable of identifying non-linear causalities and distinguishing causation from correlation.

Findings

The metric successfully detects causality in synthetic, dynamical, and brain activity data.

It outperforms classical causality metrics in non-linear scenarios.

The method works with both cross-sectional and longitudinal data.

Abstract

Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non- linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.