mlsanityData to 14 Oct 2026

Exploring Causal Learning through Graph Neural Networks: An In-depth Review

arXiv 2311.14994 ↗First posted 25 Nov 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 79 of its 275 references that are in the map. First posted on arXiv 25 Nov 2023.

Builds on

79 of 275 references.

References · 79

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38 more
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50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.

8 papers.

  1. 2024LOGIN: A Large Language Model Consulted Graph Neural Network Training Frameworkcited by 8
  2. 2023When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlookcited by 4
  3. 2024A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal Viewcited by 3
  4. 2025Causal Graph Neural Networks for Healthcarecited by 1
  5. 2026Causal Neighbourhood Learning for Invariant Graph Representationscited by 1
  6. 2025Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agendacited by 0
  7. 2025From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discoverycited by 0
  8. 2025Causal-driven attribution (CDA): Estimating channel influence without user-level datacited by 0