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

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.

Links

Published article: https://doi.org/10.1007/s10687-022-00456-4 (PDF)
Supplementary material: Supplementary Material
Code and reproducibility: https://github.com/opasche/ExtremalCausalModelling

Preprint (obsolete): https://arxiv.org/abs/2110.06686 (PDF)

Dates

First version: October 2021
Online publication: December 2022
Final issue publication: September 2023

Recommended citation: Pasche, O. C., Chavez-Demoulin, V. and Davison, A. C. (2023). "Causal modelling of heavy-tailed variables and confounders with application to river flow." Extremes 26(3), 573–594. https://doi.org/10.1007/s10687-022-00456-4
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