Loading...
A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
Li, Haoxuan Liu, Yue Geng, Zhi Zhang, Kun
Li, Haoxuan
Liu, Yue
Geng, Zhi
Zhang, Kun
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2024
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Developing fair automated machine learning algorithms is critical in making safe and trustworthy decisions. Many causality-based fairness notions have been proposed to address the above issues by quantifying the causal connections between sensitive attributes and decisions, and when the true causal graph is fully known, certain algorithms that achieve interventional fairness have been proposed. However, when the true causal graph is unknown, it is still challenging to effectively and efficiently exploit partially directed acyclic graphs (PDAGs) to achieve interventional fairness. To exploit the PDAGs for achieving interventional fairness, previous methods have been built on variable selection or causal effect identification, but limited to reduced prediction accuracy or strong assumptions. In this paper, we propose a general min-max optimization framework that can achieve interventional fairness with promising prediction accuracy and can be extended to maximally oriented PDAGs (MPDAGs) with added background knowledge. Specifically, we first estimate all possible treatment effects of sensitive attributes on a given prediction model from all possible adjustment sets of sensitive attributes via an efficient local approach. Next, we propose to alternatively update the prediction model and possible estimated causal effects, where the prediction model is trained via a min-max loss to control the worst-case fairness violations. Extensive experiments on synthetic and real-world datasets verify the superiority of our methods. To benefit the research community, we have released our project at https://github.com/haoxuanli-pku/NeurIPS24-Interventional-Fairness-with-PDAGs.
Citation
H. Li, Y. Liu, Z. Geng, and K. Zhang, “A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs,” Adv Neural Inf Process Syst, vol. 37, pp. 135415–135436, Dec. 2024, Accessed: Mar. 24, 2025. [Online]. Available: https://github.com/haoxuanli-pku/NeurIPS24-Interventional-Fairness-
Source
Advances in Neural Information Processing Systems (NeurIPS 2024)
Conference
Keywords
Language encoders, Grammatical errors, Robustness evaluation, Natural language processing, Error correction
Subjects
Source
Publisher
NEURIPS