Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization
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arXiv
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| Format: | Preprint |
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2025
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| author | Alpay, Faruk Alpay, Taylan |
| author_facet | Alpay, Faruk Alpay, Taylan |
| contents | We present Calibrated Counterfactual Conformal Fairness ($C^3F$), a post-hoc procedure that targets group-conditional coverage parity under covariate shift. $C^3F$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization Alpay, Faruk Alpay, Taylan Methodology 62G15, 62G08, 62C12, 62H30, 62G20 G.3; I.5.1; I.5.2 We present Calibrated Counterfactual Conformal Fairness ($C^3F$), a post-hoc procedure that targets group-conditional coverage parity under covariate shift. $C^3F$ 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. |
| title | Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization |
| topic | Methodology 62G15, 62G08, 62C12, 62H30, 62G20 G.3; I.5.1; I.5.2 |
| url | https://arxiv.org/abs/2509.25295 |