COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
Proceedings of the AAAI Conference on Artificial Intelligence , author=
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RepFlow combines representation learning and conditional flow matching to estimate both point and distributional causal effects while mitigating selection bias via entropically regularized Wasserstein distance on normalized latent representations.
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COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
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RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation
RepFlow combines representation learning and conditional flow matching to estimate both point and distributional causal effects while mitigating selection bias via entropically regularized Wasserstein distance on normalized latent representations.