Towards counterfactual fairness through auxiliary variables
Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai,, Yongkai Wu, Ang Li
TL;DR
This paper introduces EXOC, a causal reasoning framework using auxiliary variables to improve counterfactual fairness in machine learning models, balancing fairness and accuracy more effectively than existing methods.
Contribution
It proposes a novel EXOC framework that leverages auxiliary variables and causal reasoning to enhance counterfactual fairness in predictive models.
Findings
EXOC outperforms state-of-the-art approaches in experiments.
The framework effectively balances fairness and predictive accuracy.
Validation on synthetic and real-world datasets confirms its effectiveness.
Abstract
The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variations of sensitive attributes, which is a crucial concept in addressing societal biases. However, existing counterfactual fairness approaches usually overlook intrinsic information about sensitive features, limiting their ability to achieve fairness while simultaneously maintaining performance. To tackle this challenge, we introduce EXOgenous Causal reasoning (EXOC), a novel causal reasoning framework motivated by exogenous variables. It leverages auxiliary variables to uncover intrinsic properties that give rise to sensitive attributes. Our framework explicitly defines an…
Peer Reviews
Decision·ICLR 2025 Poster
- The proposed framework allows a trade-off between utility and fairness by adjusting the strength of the correlation between S' and S''. - In linear cases, they provide bounds on the counterfactual fairness error to show the benefit of S'. They also provide an analysis with unknown functions in the causal graph. - Strong empirical performance across different datasets and settings. - Ablation study on \gamma and S'' supported the analysis in Section 3.
- The proposed method heavily rely on the assumed causal graph in Fig. 1 (b). I wonder how general this causal graph can be. - The experiments are only conducted on two small tabular datasets.
1) EXOC introduces a new approach by utilizing auxiliary variables to capture latent information, improving counterfactual fairness and accuracy, which is new to my knowledge 2) Demonstrates competitive accuracy and fairness on benchmark datasets, outperforming prior models. Though in many cases the improvement is only with accuracy or fairness, not as impressive as claim in the intro.
1) The paper does not adequately explain the process for tuning hyperparameters, particularly within the causal structure. Specifically, the role of hyperparameter 𝛾 in balancing fairness and accuracy is crucial, yet the approach to fine-tuning this balance remains unclear. And I am wondering if a efficient tuning strategy exist as it involves a tradeoff. 2) The causal diagram in Figure 1, illustrating the roles of auxiliary nodes S' and control nodes S'' , lacks practical grounding. The authors
The introduction of auxiliary variables $S’$ and control node $S’’$ is interesting.
- The authors assert in the introduction part that their framework can "enhance fairness without compromising accuracy." However, the evidence supporting this claim is not readily discernible in the paper. A clearer demonstration or justification of this statement is needed. - The methodology section lacks clarity in several areas. For instance, in lines 199-201, the statement "As $\alpha(s*−s)$ is the counterfactual parity that plays a more important role than the standard deviation, we showca
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Taxonomy
TopicsNeural Networks and Applications