General Tests for Pairwise Causality in Extremes by Melanie Schienle

Join us for this week's Econometrics Research Seminar, Summer Term 2026

Melanie Schienle, from the Karlsruhe Institute of Technology will present this week's seminar on General Tests for Pairwise Causality in Extremes.

Abstract

Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behaviour. This paper studies the identification of causal relations in extremes within a structural causal model represented by a directed acyclic graph. We analyze the asymptotic behaviour of the Causal Tail Coefficient (CTC), a measure of causal dependence between extreme realizations of variables, when the innovations of the structural model follow regularly varying distributions. In contrast to the existing literature, we allow the variables in the system to exhibit heterogeneous tail indices and consider the presence of potentially heavy-tailed unobserved confounders. We derive theoretical results characterizing the limiting behaviour of the CTC under these conditions and show how differences in tail behaviour can provide identifying information about the causal structure. Based on these results, we propose a testing framework that distinguishes direct causal effects from confounding in heavy-tailed environments. The finite sample performance of the proposed methodology is investigated through simulation studies and illustrated using applications to climate and financial data. The results demonstrate that causal relations among extreme events can be identified even when variables exhibit substantially different tail behaviour.

This seminar will be held on campus, is open to all levels of study and is also open to the public. To register your place and gain access to the webinar, please contact the seminar organisers.

This event is part of the Econometrics Research Seminar Series.