This repository curated list of awesome Causality-Inspired Graph Neural Network (CIGNN) works and relevant resources reviewed in our survey 'When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook'. Recently, the integration of causal learning techniques into GNNs has demonstrated significant potential in mitigating trustworthiness issues. This is achieved by capturing the underlying data causality instead of relying on superficial correlations. In this survey, we comprehensively reviews the recent progress of CIGNNs within a novel taxonomy, aiming to distill the fundamental rationales behind these works in the lens of causality and spark further research on this promising direction.
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(Arxiv 2202.07987) H. Li, et al., “Out-of-distribution generalization on graphs: A survey,” CoRR, vol. abs/2202.07987, 2022. [Paper]
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(NIPS'22) GOOD: A graph out-of-distribution benchmark, [Paper], [Github]
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(Arxiv 2201.09637) Drugood: Out-of-distribution (OOD) dataset curator and benchmark for ai-aided drug discovery - A focus on affinity prediction problems with noise annotations. [Paper], [Github]
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(Arxiv 2310.03152) Towards out-of-distribution generalizable predictions of chemical kinetics properties. [Paper], [Github]
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(TPAMI'23) Explainability in graph neural networks: A taxonomic survey. [Paper]
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(CSUR'23) A survey on graph counterfactual explanations: Definitions, methods, evaluation, and research challenges. [Paper]
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(NIPS'22) GOOD: A graph out-of-distribution benchmark, [Paper], [Github]
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(CIKM'22) GRETEL: graph counterfactual explanation evaluation framework. [Paper], [Github]
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(WSDM'23) Developing and evaluating graph counterfactual explanation with GRETEL. [Paper], [Github]
- (CIKM'21) Causebox: A causal inference toolbox for benchmarking treatment effect estimators with machine learning methods. [Paper], [Github]
- (Arxiv 2307.16405) Causal-learn: Causal discovery in python. [Paper], [Github]
- (ICLR'23) 3DIdentBox: A Toolbox for Identifiability Benchmarking. [Paper], [Github]
- (Arxiv 2201.08802) Deconfounding to explanation evaluation in graph neural networks.
- (AAAI'23) Robust causal graph representation learning against confounding effects.
- (TNNLS'22) Debiased graph neural networks with agnostic label selection bias.
- (TPAMI'23) Generalizing graph neural networks on out-of-distribution graphs. [Github]
- (TKDE'23) OOD-GNN: out-of-distribution generalized graph neural network.
- (AAAI'24) Learning to reweight for generalizable graph neural network
- (SIGIR'21) Should graph convolution trust neighbors? A simple causal inference method.
- (IJCAI'23) Causal based supervision of attention in graph neural network: A better and simpler choice towards powerful attention.
- (KDD'23) Rumor detection with diverse counterfactual evidence.
- (UAI'21) Towards a unified framework for fair and stable graph representation learning.
- (ICDM'21) A multi-view confidence calibrated framework for fair and stable graph representation learning.
- (ICML'21) Generative causal explanations for graph neural networks.
- (TPAMI'23) Reinforced causal explainer for graph neural networks.
- (KDD'24) Rethinking Fair Graph Neural Networks from Re-balancing.
- (ICML'22) Learning from counterfactual links for link prediction.
- (ICDM'23) Mitigating multisource biases in graph neural networks via real counterfactual samples.
- (WSDM'22) Learning fair node representations with graph counterfactual fairness.
- (2023) Counterfactual fairness on graphs: Augmentations, hidden confounders, and identifiability.
- (AISTAT'22) Cf-gnnexplainer: Counterfactual explanations for graph neural networks.
- (IJCNN'21) Meg: Generating molecular coun- terfactual explanations for deep graph networks.
- (NIPS'22) CLEAR: genera- tive counterfactual explanations on graphs.
- (WWW'22) Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning.
- (KDD'21) Counterfactual graphs for explainable classification of brain networks.
- (NIPS'21) Robust counterfactual explanations on graph neural networks.
- (ICDM'21) Multi-objective explanations of GNN predictions.
- (WSDM'23) Global counterfactual explainer for graph neural networks.
- (WWW'24) Game-theoretic counterfactual explanation for graph neural networks.
- (AAAI'24) Rethinking causal relationships learning in graph neural networks.
- (KBS'24) Introducing diminutive causal structure into graph representation learning.
- (ICLR'22) Handling distribution shifts on graphs: An invariance perspective.
- (ICLR'22) Discovering invariant rationales for graph neural networks.
- (CVPR'23) Mind the label shift of augmentation-based graph OOD generalization.
- (NIPS'22) Learning invariant graph representations for out-of-distribution generalization.
- (ICDE'23) BA-GNN: on learning bias- aware graph neural network.
- (TOIS'24) Invariant node representation learning under distribution shifts with multiple latent environments.
- (KBS'24) Fortune favors the invariant: Enhancing GNNs' generalizability with Invariant Graph Learning.
- (KDD'22) Causal atten- tion for interpretable and generalizable graph classification.
- (NIPS'22) Debiasing graph neural networks via learning disentangled causal substructure.
- (NIPS'22) Learning substructure invariance for out-of-distribution molecular representations.
- (NIPS'22) Learning causally invariant representa- tions for out-of-distribution generalization on graphs.
- (CVPR'22) Orphicx: A causality-inspired latent variable model for interpreting graph neural networks.
- (KDD'23) Shift-robust molecular relational learning with causal substructure.
- (NIPS'23) Does invariant graph learning via environment augmentation learn invariance?
- (NIPS'23) Joint learning of label and environment causal independence for graph out-of-distribution generalization.
- (CIKM'23) Causality and independence enhancement for biased node classification.
- (CIKM'23) Towards fair graph neural networks via graph counterfactual.
- (AAAI'24) A twist for graph classification: Optimizing causal information flow in graph neural networks.
- (WWW'24) Graph out-of-distribution generalization via causal intervention.
- (ICML'24) Learning Divergence Fields for Shift-Robust Graph Representations.
- (2024) Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Network.
- (2024) Unifying invariance and spuriousity for graph out-of- distribution via probability of necessity and sufficiency.
- (2024) CI-GNN: A granger causality- inspired graph neural network for interpretable brain network- based psychiatric diagnosis.
- (2024) Enhancing graph neural networks for self-explainable modeling: A causal perspective with multi-granularity receptive fields.
- (ICML'22) Let invariant rationale discovery inspire graph contrastive learning.
- (KDD'23) FLOOD: A flexible invariant learning framework for out-of-distribution generalization on graphs.
- (NIPS'23) Learning invariant molecular representation in latent discrete space.
- (WWW'23) Generating counterfactual hard negative samples for graph contrastive learning.
- (AAAI'24) Graph contrastive invariant learning from the causal perspective.
- (NN'25) Disentangled contrastive learning for fair graph representations.
We appreciate your kind contributions and suggestions! If you know of any causality-inspired GNN works that are not listed, feel free to open an issue. We will update them in our survey and repository.
This project is licensed under the MIT License.
If you find our work useful for your research, please consider citing
@article{jiang2023survey,
title={When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook},
author={Jiang, Wenzhao and Liu, Hao and Xiong, Hui},
journal={arXiv preprint arXiv:2312.12477},
year={2024}
}