CausalPre: Scalable and Effective Data Pre-processing for Causal Fairness

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. T o ensure scalability, CausalPre adopts a carefully crafted variant of low-dimensional marginal factorization to approximate the joint distribution, complemented by a heuristic algorithm that efficiently tackles the associated computational challenge. Extensive experiments on benchmark datasets demonstrate that CausalPre is both effective and scalable, challenging the conventional belief that achieving causal fairness requires trading off relationship coverage for relaxed model assumptions. Machine learning (ML) systems are increasingly integrated into decision-making processes in domains such as education [1], finance [2], employment [3], advertising [4], and law enforcement [5], [6]. While these systems offer efficiency and scalability, they also pose serious concerns about fairness [7]- [14]. In particular, their reliance on historical data can unintentionally amplify biases, producing inaccurate, discriminatory outcomes with severe real-world impacts in high-stakes areas like criminal justice. These concerns have motivated the development of fairness-aware data pre-processing techniques within database management systems (DBMS) [15]-[22]. Compared to traditional fairness interventions at the model training or inference stages [23]-[28], pre-processing methods offer: (i) a once-for-all benefit, meaning that once data is calibrated for fairness, it can be used in any downstream task, regardless of the ML model employed; and (ii) a user-friendly workflow, as fairness considerations are directly embedded into the data pre-processing pipeline, enabling practitioners to focus on the downstream task without specialized fairness expertise. A straightforward approach to achieve this is to remove all sensitive attributes (e.g., gender and race) from the training data. However, such ad hoc solutions often fail in practice, as non-sensitive attributes may act as proxies for sensitive ones, particularly when strong correlations exist [18], [29].