[2509.15199] CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness

Computer Science > Machine Learning arXiv:2509.15199 (cs) [Submitted on 18 Sep 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness Authors:Ying Zheng, Yangfan Jiang, Kian-Lee Tan View a PDF of the paper titled CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness, by Ying Zheng and 2 other authors View PDF HTML (experimental) Abstract:Causal fairness in databases is crucial to preventing biased and inaccurate outcomes in downstream tasks. While most prior work assumes a known causal model, recent efforts relax this assumption by enforcing additional constraints. However, these approaches often fail to capture broader attribute relationships that are critical to maintaining utility. This raises a fundamental question: Can we harness the benefits of causal reasoning to design efficient and effective fairness solutions without relying on strong assumptions about the underlying causal model? In this paper, we seek to answer this question by introducing CausalPre, a scalable and effective causality-guided data pre-processing framework that guarantees justifiable fairness, a strong causal notion of fairness. CausalPre extracts causally fair relationships by reformulating the originally complex and computationally infeasible extraction task into a tailored distribution estimation problem. To ensure scalability, CausalPre adopts a carefully crafted variant of low-dimensional mar...