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

Simi Job, Xiaohui Tao, Taotao Cai, Haoran Xie, Jianming Yong, Xin Wang

TL;DR

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 is proposed.

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.

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

TL;DR

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 is proposed.

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.
Paper Structure (21 sections, 5 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 5 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Graph Neural Networks
  4. Causality and Graph Neural Networks
  5. Preliminaries
  6. Graph Attention Networks
  7. Causal Learning
  8. Methodology
  9. Problem Definition
  10. Causal Confounder-Aware Graph Neural Networks (CCAGNN)
  11. Feature Encoding
  12. Graph Augmentation and Intervention-Aware Processing
  13. Mutual Information Estimation
  14. Interventional Training for Causal Learning
  15. Experiments
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Traditional vs causal GNN in predicting patient recovery; causal GNN isolates medication's true effect by adjusting for confounders (age, other patient factors); intervention-based causal GNN models treatment changes for robust predictions.
  • Figure 2: Causal graphs: (a) Z influences both X and Y; (b) Isolates true effect of X on Y, independent of Z
  • Figure 3: CCAGNN Architecture: (i) Node features are split into causal and non-causal parts using gated GAT layers for structure-aware encoding; (ii) Hybrid feature masking, attention-guided noise and structural changes simulate interventions, processed through dual GAT pipelines for causal and non-causal features; (iii) A dual-encoder estimates and minimizes mutual information between causal and non-causal features with a confidence-based fusion gate for disentangled representations; (iv) Counterfactual interventions and multi-branch learning with gated fusion allows robust predictions.
  • Figure 4: Mutual Information Loss Plots
  • Figure 5: Ablation study results showing F-scores for different model configurations across datasets.