@inproceedings{
zhou2024counterfactual,
title={Counterfactual Fairness by Combining Factual and Counterfactual Predictions},
author={Zeyu Zhou and Tianci Liu and Ruqi Bai and Jing Gao and Murat Kocaoglu and David I. Inouye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J0Itri0UiN}
}
In high-stakes 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 remain largely unclear.
To fill 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 with a minimal loss of performance.
By analyzing the excess risk incurred by perfect 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 this, we propose a practical algorithm that can be applied in such scenarios. 
Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods.
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Zeyu Zhou Tianci Liu Ruqi Bai Jing Gao Murat Kocaoglu David I. Inouye