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 the training
data and cannot model the casual relations. Recently, counterfactual learning
on graphs has shown promising results in alleviating these drawbacks. Various
graph counterfactual learning 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 research 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
this https URL

本综述分四个主要方向，对图形反事实学习的现有方法进行分类和全面的审查。我们提出有前途的未来研究方向，并为未来研究编制了一份资源清单。我们还提供了一份开放源代码实现、公共数据集和常用评估度量标准的列表。本文的目的是为构建图形反事实学习类别和当前资源的统一理解提供 “一站式” 服务，并将维护论文和资源的知识库，并将不断更新此资源。