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Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks

Xiaojing Du, Feiyu Yang, Wentao Gao, Xiongren Chen

TL;DR

CgNN is a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation and offers a robust GNN-driven IV framework for causal inference in complex network data.

Abstract

As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the strong ignorability assumption, which presumes the absence of hidden confounders-an assumption that is both difficult to validate and often unrealistic in practice. To address this issue, we propose CgNN, a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation. By utilizing network structure as IVs, we reduce confounder bias while preserving the correlation with treatment. Our integration of attention mechanisms enhances robustness and improves the identification of important nodes. Validated on two real-world datasets, our results demonstrate that CgNN effectively mitigates hidden confounder bias and offers a robust GNN-driven IV framework for causal inference in complex network data.

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks

TL;DR

CgNN is a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation and offers a robust GNN-driven IV framework for causal inference in complex network data.

Abstract

As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the strong ignorability assumption, which presumes the absence of hidden confounders-an assumption that is both difficult to validate and often unrealistic in practice. To address this issue, we propose CgNN, a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation. By utilizing network structure as IVs, we reduce confounder bias while preserving the correlation with treatment. Our integration of attention mechanisms enhances robustness and improves the identification of important nodes. Validated on two real-world datasets, our results demonstrate that CgNN effectively mitigates hidden confounder bias and offers a robust GNN-driven IV framework for causal inference in complex network data.
Paper Structure (12 sections, 13 equations, 4 figures, 2 tables)

This paper contains 12 sections, 13 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. The Proposed CgNN Method
  4. Estimation Targets and Assumptions
  5. Main effects
  6. Peer effects
  7. Total effects
  8. Implementation
  9. Experiment
  10. Datasets
  11. Simulation
  12. Conclusion

Figures (4)

  • Figure 1: Causal DAGs illustrating challenges in causal effect estimation within networks. $\mathbf{X}$ and $\mathbf{U}$ are observed features and hidden confounders, $\mathbf{T}$ and $\mathbf{Y}$ represent treatment and outcome. (a) assumes strong ignorability, (b) includes hidden confounder $\mathbf{U}$.
  • Figure 2: (a) illustrates the network structure. (b) focuses on node 2, which has nodes 1 and 3 as its peers within the network.
  • Figure 3: The workflow of our CgNN method for estimating ME, PE and TE within network data.
  • Figure 4: The results demonstrate how the counterfactual estimation error ($\epsilon_{MSE}$) correlates with the proportion of units experiencing treatment flips.