A pipeline for enabling path-specific causal fairness in observational health data

2026-01-14

AI Summary

This work proposes a general, model-agnostic causal fairness training framework to address the risk that medical machine learning models may replicate or exacerbate biases in healthcare. For the first time in a clinical context, the approach disentangles direct and indirect pathways of bias by leveraging structural causal models to map path-specific causal fairness constraints onto observational health data. The method employs an unconstrained base model to generate downstream predictions, thereby preserving predictive accuracy while enabling generalizable fairness improvements. Furthermore, it systematically characterizes the trade-off between fairness and accuracy, offering a principled way to balance these often competing objectives in real-world medical applications.

Technology Category

• Machine Learning: Causal Learning
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare biases. Although many definitions of fairness exist, we focus on path-specific causal fairness, which allows us to better consider the social and medical contexts in which biases occur (e.g., direct discrimination by a clinician or model versus bias due to differential access to the healthcare system) and to characterize how these biases may appear in learned models. In this work, we map the structural fairness model to the observational healthcare setting and create a generalizable pipeline for training causally fair models. The pipeline explicitly considers specific healthcare context and disparities to define a target"fair"model. Our work fills two major gaps: first, we expand on characterizations of the"fairness-accuracy"tradeoff by detangling direct and indirect sources of bias and jointly presenting these fairness considerations alongside considerations of accuracy in the context of broadly known biases. Second, we demonstrate how a foundation model trained without fairness constraints on observational health data can be leveraged to generate causally fair downstream predictions in tasks with known social and medical disparities. This work presents a model-agnostic pipeline for training causally fair machine learning models that address both direct and indirect forms of healthcare bias.

Problem

• causal fairness
• healthcare bias
• path-specific fairness
• observational health data
• fairness-accuracy tradeoff

Innovation

• path-specific causal fairness
• observational health data
• fairness-accuracy tradeoff
• causal fairness pipeline
• healthcare bias