Consistent End-to-End Estimation for Counterfactual Fairness

TL;DR

This paper introduces a new neural network-based method for counterfactual fairness that provides theoretical guarantees and achieves state-of-the-art results across multiple datasets.

Contribution

We propose a novel counterfactual fairness predictor with theoretical guarantees, directly learning counterfactual distributions via tailored neural networks and a new regularization technique.

Findings

Method achieves state-of-the-art performance on various datasets.

Provides theoretical guarantees for counterfactual fairness.

Outperforms existing baselines in fairness and accuracy.

Abstract

Fairness in predictions is of direct importance in practice due to legal, ethical, and societal reasons. This is often accomplished through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute. However, achieving counterfactual fairness is challenging as counterfactuals are unobservable, and, because of that, existing baselines for counterfactual fairness do not have theoretical guarantees. In this paper, we propose a novel counterfactual fairness predictor for making predictions under counterfactual fairness. Here, we follow the standard counterfactual fairness setting and directly learn the counterfactual distribution of the descendants of the sensitive attribute via tailored neural networks, which we then use to enforce fair predictions through a novel counterfactual mediator regularization. Unique to our work is that we provide theoretical guarantees that our method is effective in ensuring the notion of counterfactual fairness. We further compare the performance across various datasets, where our method achieves state-of-the-art performance.