Causal Fairness for Decomposing Racial and Sex Disparities in Treatment Allocation Using Real-World Data

Presentation Time: 09:15 AM - 09:30 AM

Abstract Keywords: Causal Inference, Health Equity, Data Mining
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics

Recent efforts toward equitable healthcare have highlighted disparities in treatment allocation based on race, sex, socioeconomic status, and health insurance access, emphasizing the need for fairness in clinical decisions. However, the mechanisms behind these disparities are not well understood due to limited datasets and the complexity of causal inference methods. To address this, we introduce a causal fairness analysis (CFA) framework to assess the impact of race and sex on treatment decisions using electronic health record (EHR) data. This framework combines causal mediation analysis and double machine learning to explore how protected attributes influence clinical decisions. Our study focuses on decomposing treatment disparities for coronary artery disease (CAD), examining direct, indirect (through social and clinical health determinants), and confounded effects (by other demographics). This innovative approach aims to provide a detailed understanding of healthcare disparities, shedding light on potential biases and discrimination, and advancing the quest for more equitable healthcare solutions.

Speaker(s):
Linying Zhang, PhD
Washington University in St. Louis

Author(s):
Linying Zhang, PhD - Washington University in St. Louis; Xinzhuo Jiang, MS - Columbia University Department of Biomedical Informatics; Karthik Natarajan, PhD - Columbia University Dept of Biomedical Informatics; George Hripcsak, MD - Columbia University Irving Medical Center;