Derives closed-form optimal counterfactually fair regressor via barycentric quantile map and proves Õ(n^{-1/3}) finite-sample fairness and risk bounds for discretized post-processing under mild assumptions.
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The Pareto frontier of fair algorithmic decisions consists of deterministic group-specific threshold rules on predicted success probabilities, which can include upper bounds for some fairness metrics and holds independently of model training approach.
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Counterfactually Fair Regression via Optimal Transport
Derives closed-form optimal counterfactually fair regressor via barycentric quantile map and proves Õ(n^{-1/3}) finite-sample fairness and risk bounds for discretized post-processing under mild assumptions.