Counterfactual Fairness by Combining Factual and Counterfactual Predictions
<|reference_start|>Counterfactual Fairness by Combining Factual and Counterfactual Predictions: In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group. Previous works have proposed methods that guarantee CF. Notwithstanding, their effects on the model's predictive performance remains largely unclear. To fill in this gap, we provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner. We first propose a simple but effective method to cast an optimal but potentially unfair predictor into a fair one without losing the optimality. By analyzing its excess risk in order to achieve CF, we quantify this inherent trade-off. Further analysis on our method's performance with access to only incomplete causal knowledge is also conducted. Built upon it, we propose a performant algorithm that can be applied in such scenarios. Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods.<|reference_end|>
@article{zhou2024counterfactual,
    title={Counterfactual Fairness by Combining Factual and Counterfactual
  Predictions},
    author={Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I.
  Inouye},
    journal={arXiv preprint arXiv:2409.01977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01977},
    primaryClass={cs.LG}
}