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.

翻译：摘要：本文总结了极值因果推断领域的最新方法。我们以非穷尽的方式，首先描述了一种处理分位数处理效应的极值方法，其中处理对结果的尾部产生影响。随后，我们深入探讨了两种主要的极值因果结构，并对其可识别性提供了深刻见解。此外，我们讨论了与这两种模型相关的因果结构学习，以及模型无关框架中的因果结构学习。为了展示这些方法的实用性，我们使用塞纳河网络数据集应用并比较了这些不同方法。本文最后进行了总结，并概述了未来研究的潜在方向。