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 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.

翻译：数据库中的因果公平性对于防止下游任务出现有偏且不准确的结果至关重要。尽管大多数先前研究假设因果模型已知，近期的研究通过施加额外约束来放宽这一假设。然而，这些方法往往无法捕捉对维持效用至关重要的更广泛的属性关系。这引发了一个根本性问题：我们能否利用因果推理的优势，在不依赖对底层因果模型的强假设的情况下，设计出高效且有效的公平性解决方案？在本文中，我们通过引入CausalPre来回答这一问题，CausalPre是一个可扩展且有效的因果引导数据预处理框架，它保证了可证明公平性——一种强因果公平概念。CausalPre通过将原本复杂且计算不可行的提取任务重新表述为一个定制化的分布估计问题，来提取因果公平关系。为确保可扩展性，CausalPre采用了一种精心设计的低维边际分解变体来近似联合分布，并辅以一种启发式算法来高效应对相关的计算挑战。在基准数据集上的大量实验表明，CausalPre既有效又可扩展，挑战了实现因果公平需要以关系覆盖度为代价来换取放宽模型假设的传统观点。