# Counterfactual Learning on Graphs: A Survey

arXiv: https://arxiv.org/abs/2304.01391 · HTML: https://mlsanity.com/paper/2304.01391/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 136 of its 249 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 30 of 136)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Just Train Twice: Improving Group Robustness without Training Group Information](https://mlsanity.com/paper/2107.09044/) (2021)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)

## Built on by (most cited first, 22 of 22)

- [Simple and Asymmetric Graph Contrastive Learning without Augmentations](https://mlsanity.com/paper/2310.18884/) (2023)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [Towards Fair Graph Neural Networks via Graph Counterfactual](https://mlsanity.com/paper/2307.04937/) (2023)
- [How Interpretable Are Interpretable Graph Neural Networks?](https://mlsanity.com/paper/2406.07955/) (2024)
- [From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context](https://mlsanity.com/paper/2508.07117/) (2025)
- [When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook](https://mlsanity.com/paper/2312.12477/) (2023)
- [Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection](https://mlsanity.com/paper/2407.13251/) (2024)
- [A Recipe for Causal Graph Regression: Confounding Effects Revisited](https://mlsanity.com/paper/2507.00440/) (2025)
- [Counterfactual Graph Transformer for Traffic Flow Prediction](https://mlsanity.com/paper/2308.00391/) (2023)
- [Counterfactual Reasoning with Knowledge Graph Embeddings](https://mlsanity.com/paper/2403.06936/) (2024)
- [Fair Graph Neural Network with Supervised Contrastive Regularization](https://mlsanity.com/paper/2404.06090/) (2024)
- [Rethinking Distance Metrics for Counterfactual Explainability](https://mlsanity.com/paper/2410.14522/) (2024)
- [Finding Counterfactual Evidences for Node Classification](https://mlsanity.com/paper/2505.11396/) (2025)
- [Improving Fairness in Graph Neural Networks via Counterfactual Debiasing](https://mlsanity.com/paper/2508.14683/) (2025)
- [Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection](https://mlsanity.com/paper/2508.10785/) (2025)
- [Heterogeneous Graph Alignment for Joint Reasoning and Interpretability](https://mlsanity.com/paper/2601.22593/) (2026)
- [Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay](https://mlsanity.com/paper/2605.00374/) (2026)
- [A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks](https://mlsanity.com/paper/2606.22033/) (2026)
- [NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption](https://mlsanity.com/paper/2608.16038/) (2026)
- [A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit](https://mlsanity.com/paper/2609.05113/) (2026)
- [From Explanations to Interventions: Execution-Guided Counterfactual Synthesis in Temporal Graphs](https://mlsanity.com/paper/2609.11170/) (2026)
- [Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs](https://mlsanity.com/paper/2609.37667/) (2026)

## Abstract (arXiv)

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.