ArXiv Preprint
Fairness in clinical decision-making is a critical element of health equity,
but assessing fairness of clinical decisions from observational data is
challenging. Recently, many fairness notions have been proposed to quantify
fairness in decision-making, among which causality-based fairness notions have
gained increasing attention due to its potential in adjusting for confounding
and reasoning about bias. However, causal fairness notions remain
under-explored in the context of clinical decision-making with large-scale
healthcare data. In this work, we propose a Bayesian causal inference approach
for assessing a causal fairness notion called principal fairness in clinical
settings. We demonstrate our approach using both simulated data and electronic
health records (EHR) data.
Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak
2022-11-21
