Calibrated Counterfactual Conformal Fairness ($C^{3}F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization

Abstract

We present Calibrated Counterfactual Conformal Fairness ($C^{3}F$), a post-hoc procedure that targets group-conditional coverage parity under covariate shift. $C^{3}F$ combines importance-weighted conformal calibration with a counterfactual regularizer based on path-specific effects in a structural causal model. The method estimates group-specific nonconformity quantiles using likelihood-ratio weights so that coverage degrades gracefully with the second moment of the weights. We derive finite-sample lower bounds on group-wise coverage and a bound on the equalized conditional coverage gap, and we show first-order control of a counterfactual coverage-parity surrogate via smooth threshold regularization. The approach is model-agnostic, label-efficient, and deployable without retraining. Empirical evaluations on standard classification benchmarks demonstrate improved group-conditional coverage and competitive efficiency relative to shift-aware and fairness-oriented conformal baselines. We discuss practical considerations, including partial availability of sensitive attributes and robustness to structural causal misspecification.

5 pages