因果性与极值
Causality and extremes
AI总结：
该研究总结极值因果推断的前沿方法，介绍分位数处理效应的极值方法、两类极值因果结构的可识别性及相关因果结构学习，并用Seine network数据集验证实用性，指明未来研究方向。
摘要：
在本研究中，我们总结了极值因果推断领域的前沿方法。我们以非穷尽的方式，首先介绍了一种用于分位数处理效应的极值方法，该方法适用于处理会影响结果变量尾部的情况。随后，我们深入探讨了两种主要的极值因果结构，对其可识别性给出了深入见解。此外，我们还讨论了这两类模型下以及模型无关框架中的因果结构学习问题。为说明这些方法的实用性，我们使用Seine network数据集对不同方法进行了应用与比较。本研究最后进行了总结，并概述了未来的潜在研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
