Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a ``one-stop-shop'' for building a unified understanding of graph counterfactual learning categories and current resources. We also maintain a repository for papers and resources and will keep updating the repository https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning.

翻译：图结构数据在现实世界中无处不在，例如社交网络、分子图和交易网络。图神经网络（GNNs）在图表示学习方面取得了巨大成功，促进了各种下游任务的发展。然而，GNNs 存在若干缺点，如缺乏可解释性、容易继承数据偏差，且无法建模因果关系。近年来，图上的反事实学习在缓解这些缺点方面展现出令人瞩目的成果。针对反事实公平性、可解释性、链接预测及其他图上的应用，已有多种方法被提出。为促进这一有前景方向的发展，本综述对图反事实学习的相关论文进行了分类与全面回顾。我们根据所研究的问题将现有方法分为四类。针对每一类别，我们提供了背景和动机性实例、总结现有工作的通用框架，并对这些工作进行了详细评述。我们指出了图结构数据、反事实学习与现实应用交叉领域中有潜力的未来研究方向。为提供未来研究的全面资源视图，我们汇集了开源实现、公共数据集及常用评估指标的集合。本综述旨在作为“一站式”资源，帮助建立对图反事实学习类别及其当前资源的统一理解。我们还维护了一个论文与资源仓库，并将持续更新：https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning。