  Healthcare continues to grapple with the persistent issue of treatment
disparities, sparking concerns regarding the equitable allocation of treatments
in clinical practice. While various fairness metrics have emerged to assess
fairness in decision-making processes, a growing focus has been on
causality-based fairness concepts due to their capacity to mitigate confounding
effects and reason about bias. However, the application of causal fairness
notions in evaluating the fairness of clinical decision-making with electronic
health record (EHR) data remains an understudied domain. This study aims to
address the methodological gap in assessing causal fairness of treatment
allocation with electronic health records data. We propose a causal fairness
algorithm to assess fairness in clinical decision-making. Our algorithm
accounts for the heterogeneity of patient populations and identifies potential
unfairness in treatment allocation by conditioning on patients who have the
same likelihood to benefit from the treatment. We apply this framework to a
patient cohort with coronary artery disease derived from an EHR database to
evaluate the fairness of treatment decisions. In addition, we investigate the
impact of social determinants of health on the assessment of causal fairness of
treatment allocation.
