Research map / Learning theory and algorithms / Fairness
Counterfactual Fairness
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a counterfactually fair latent-knowledge predictor instead of all features | Law school success prediction · Law school success test set | RMSE: 0.873 -> 0.929 | Hurt |
| Use a counterfactually fair additive-error predictor instead of all features | Law school success prediction · Law school success test set | RMSE: 0.873 -> 0.918 | Hurt |
| Use additive error terms rather than latent knowledge | Counterfactually fair law school success prediction · Law school success test set | RMSE: 0.929 -> 0.918 | Helped |
Closest papers by meaning
- Counterfactual Fairness by Combining Factual and Counterfactual Predictions (NeurIPS 2024 · similarity 0.94)
- FairPFN: A Tabular Foundation Model for Causal Fairness (ICML 2025 · similarity 0.90)
- Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness (ICML 2025 · similarity 0.90)
- Towards counterfactual fairness through auxiliary variables (ICLR 2025 · similarity 0.89)
- Fairness-Accuracy Trade-Offs: A Causal Perspective (AAAI 2025 · similarity 0.89)
- Causal Bias Detection in Generative Artifical Intelligence (NeurIPS 2026 · similarity 0.88)
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