In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning.

图神经网络 (GNNs) 在因果学习中的应用和发展进行了全面的综述，包括分类和应用等方面，提供了与实际研究相关的数据集，同时探讨了因果学习在不同领域的应用，为机器学习这一快速发展领域的挑战和未来的研究方向提供了启示。