Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

• URL: http://arxiv.org/abs/2602.17941v1
• Date: Fri, 20 Feb 2026 02:19:20 GMT
• Title: Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
• Authors: Simi Job, Xiaohui Tao, Taotao Cai, Haoran Xie, Jianming Yong, Xin Wang,
• Abstract summary: Causal learning helps to understand cause-effect relationships rather than mere associations.<n>Traditional graph machine learning methods rely on correlations and are sensitive to spurious patterns and distribution changes.<n>We propose CCAGNN, a Confounder-Aware causal GNN framework that incorporates causal reasoning into graph learning.
• Score: 10.98141213691198
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve complex problems involving relationships and connections. Causal learning is especially important in this context, since it helps to understand cause-effect relationships rather than mere associations. Since many real-world systems are inherently causal, graphs can efficiently model these systems. However, traditional graph machine learning methods including graph neural networks (GNNs), rely on correlations and are sensitive to spurious patterns and distribution changes. On the other hand, causal models enable robust predictions by isolating true causal factors, thus making them more stable under such shifts. Causal learning also helps in identifying and adjusting for confounders, ensuring that predictions reflect true causal relationships and remain accurate even under interventions. To address these challenges and build models that are robust and causally informed, we propose CCAGNN, a Confounder-Aware causal GNN framework that incorporates causal reasoning into graph learning, supporting counterfactual reasoning and providing reliable predictions in real-world settings. Comprehensive experiments on six publicly available datasets from diverse domains show that CCAGNN consistently outperforms leading state-of-the-art models.

Related papers

• Causal Neighbourhood Learning for Invariant Graph Representations [8.854365724044984]
  We propose Causal Neighbourhood Learning with Graph Neural Networks (CNL-GNN), a novel framework that performs causal interventions on graph structure.<n>CNL-GNN effectively identifies and preserves causally relevant connections and reduces spurious influences.<n>Our approach improves causal graph learning beyond traditional feature-based methods, resulting in a robust classification model.
  arXiv  Detail & Related papers  (2026-02-20T01:52:58Z)
• Generalization of Graph Neural Networks is Robust to Model Mismatch [84.01980526069075]
  Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks supported by their generalization capabilities. In this paper, we examine GNNs that operate on geometric graphs generated from manifold models. Our analysis reveals the robustness of the GNN generalization in the presence of such model mismatch.
  arXiv  Detail & Related papers  (2024-08-25T16:00:44Z)
• CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning [7.738230390425271]
  We propose an innovative Causal Enhanced Graph Representation Learning framework for TKGR (named CEGRL-TKGR)<n>This framework introduces causal structures in graph-based representation learning to unveil the essential causal relationships between events.<n>We also advocate the utilization of causal representations for predictions, aiming to mitigate the effects of erroneous correlations caused by confounding features.
  arXiv  Detail & Related papers  (2024-08-15T03:34:53Z)
• Introducing Diminutive Causal Structure into Graph Representation Learning [19.132025125620274]
  We introduce a novel method that enables Graph Neural Networks (GNNs) to glean insights from specialized diminutive causal structures. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures.
  arXiv  Detail & Related papers  (2024-06-13T00:18:20Z)
• Causal Graph Neural Networks for Wildfire Danger Prediction [25.12733727343395]
  Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. We argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires.
  arXiv  Detail & Related papers  (2024-03-13T10:58:55Z)
• Graph Out-of-Distribution Generalization via Causal Intervention [69.70137479660113]
  We introduce a conceptually simple yet principled approach for training robust graph neural networks (GNNs) under node-level distribution shifts. Our method resorts to a new learning objective derived from causal inference that coordinates an environment estimator and a mixture-of-expert GNN predictor. Our model can effectively enhance generalization with various types of distribution shifts and yield up to 27.4% accuracy improvement over state-of-the-arts on graph OOD generalization benchmarks.
  arXiv  Detail & Related papers  (2024-02-18T07:49:22Z)
• Learning to Reweight for Graph Neural Network [63.978102332612906]
  Graph Neural Networks (GNNs) show promising results for graph tasks. Existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability.
  arXiv  Detail & Related papers  (2023-12-19T12:25:10Z)
• Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure [46.86463923605841]
  We present a graph classification investigation on the training graphs with severe bias. We discover that GNNs always tend to explore the spurious correlations to make decision. We propose a general disentangled GNN framework to learn the causal substructure and bias substructure.
  arXiv  Detail & Related papers  (2022-09-28T13:55:52Z)
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs [51.33152272781324]
  Graph Neural Networks (GNNs) are proposed without considering the distribution shifts between training and testing graphs. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. We propose a general causal representation framework, called StableGNN, to eliminate the impact of spurious correlations.
  arXiv  Detail & Related papers  (2021-11-20T18:57:18Z)
• Learning Causal Models Online [103.87959747047158]
  Predictive models can rely on spurious correlations in the data for making predictions. One solution for achieving strong generalization is to incorporate causal structures in the models. We propose an online algorithm that continually detects and removes spurious features.
  arXiv  Detail & Related papers  (2020-06-12T20:49:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.