Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow

TL;DR

This paper introduces a novel causal discovery method tailored for heavy-tailed variables, effectively addressing confounding effects in extreme events, with applications demonstrated on river flow and precipitation data.

Contribution

It presents a new causal discovery approach for heavy-tailed data, including a parametric estimator and permutation test, improving causal inference in extreme event scenarios.

Findings

Methods perform well in simulations

Effective removal of confounding effects in heavy-tailed variables

Successful application to river flow and precipitation data

Abstract

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the effect of a known potential confounder to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We also introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset.