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arXiv · 2510.08724

Counterfactually Fair Conformal Prediction

Ozgur Guldogan·Neeraj Sarna·Yuanyuan Li·Michael Berger
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Abstract

While counterfactual fairness of point predictors is well studied, its extension to prediction sets--central to fair decision-making under uncertainty--remains underexplored. On the other hand, conformal prediction (CP) provides efficient, distribution-free, finite-sample valid prediction sets, yet does not ensure counterfactual fairness. We close this gap by developing Counterfactually Fair Conformal Prediction (CF-CP) that produces counterfactually fair prediction sets. Through symmetrization of conformity scores across protected-attribute interventions, we prove that CF-CP results in counterfactually fair prediction sets while maintaining the marginal coverage property. Furthermore, we empirically demonstrate that on both synthetic and real datasets, across regression and classification tasks, CF-CP achieves the desired counterfactual fairness and meets the target coverage rate with minimal increase in prediction set size. CF-CP offers a simple, training-free route to counterfactually fair uncertainty quantification.

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Ozgur Guldogan, Neeraj Sarna, Yuanyuan Li, Michael Berger. 2025-10-09. Counterfactually Fair Conformal Prediction. https://arxiv.org/abs/2510.08724

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