Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability
Abstract: We consider augmenting graph data with counterfactual generation in order to achieve fairness on downstream tasks. While this direction has been explored previously, existing methods invariably consider oversimplified causal relationships. Moreover, they often rely on unidentifiable models to encode causal relationships, making it hard to identify the true joint distribution and thus recover counterfactual graphs. To tackle these challenges, we introduce a causal model with hidden confounders on graphs, which considers the existence of hidden confounders affecting both node features and graph structures. We use an identifiable graph VAE model to simultaneously estimate hidden confounders and learn generation functions of the causal model. By incorporating a Gaussian mixture prior distribution, we improve the identifiability of our model to recover the joint distribution of observed data and hidden confounders. Using the generated counterfactual graphs, we enforce consistency in the predictions of classifiers for different counterfactual graphs, thereby achieving graph counterfactual fairness in these classifiers. Experimental results demonstrate the effectiveness of our method in improving the counterfactual fairness of classifiers on various graph tasks. Moreover, theoretical analysis, coupled with empirical results, illustrates the capability of our method to successfully identify hidden confounders.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: All changes are marked in blue.
Assigned Action Editor: ~Mingming_Gong1
Submission Number: 3422
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