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

2025-09-18

AI Summary

Causal fairness in databases often relies on strong causal model assumptions—such as known causal graphs—limiting practical applicability. Method: We propose the first efficient preprocessing framework for causal fairness that operates without an explicit causal graph. It reformulates complex causal relationship modeling as a low-dimensional marginal distribution approximation problem, leveraging an improved marginal factorization scheme and a heuristic optimization algorithm to balance fairness and utility under distribution reconstruction. The approach integrates causal inference with nonparametric distribution estimation, relaxing strict modeling assumptions while preserving broad attribute dependencies. Contribution/Results: Extensive experiments on multiple benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, achieving simultaneous improvements in fairness guarantees, computational efficiency, and scalability—thereby breaking the longstanding trade-off among strong assumptions, high computational overhead, and narrow applicability in causal fairness.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• Achieving causal fairness without strong causal model assumptions
• Capturing broad attribute relationships while maintaining data utility
• Designing scalable pre-processing for causally fair data extraction

Innovation

• Causality-guided data pre-processing framework
• Reformulates extraction into distribution estimation
• Uses low-dimensional marginal factorization approximation