Causality and extremes

TL;DR

This paper reviews state-of-the-art causal inference methods for extreme events, focusing on tail impacts, causal structures, and learning approaches, supported by practical application to a real dataset.

Contribution

It provides a comprehensive summary of current methods for causal inference in extremes, including new insights into model identifiability and structure learning.

Findings

Comparison of causal methods on Seine network data

Insights into identifiability of extremal causal structures

Discussion of future research directions

Abstract

In this work, we summarize the state-of-the-art methods in causal inference for extremes. In a non-exhaustive way, we start by describing an extremal approach to quantile treatment effect where the treatment has an impact on the tail of the outcome. Then, we delve into two primary causal structures for extremes, offering in-depth insights into their identifiability. Additionally, we discuss causal structure learning in relation to these two models as well as in a model-agnostic framework. To illustrate the practicality of the approaches, we apply and compare these different methods using a Seine network dataset. This work concludes with a summary and outlines potential directions for future research.