  The use of machine learning systems to support decision making in healthcare
raises questions as to what extent these systems may introduce or exacerbate
disparities in care for historically underrepresented and mistreated groups,
due to biases implicitly embedded in observational data in electronic health
records. To address this problem in the context of clinical risk prediction
models, we develop an augmented counterfactual fairness criteria to extend the
group fairness criteria of equalized odds to an individual level. We do so by
requiring that the same prediction be made for a patient, and a counterfactual
patient resulting from changing a sensitive attribute, if the factual and
counterfactual outcomes do not differ. We investigate the extent to which the
augmented counterfactual fairness criteria may be applied to develop fair
models for prolonged inpatient length of stay and mortality with observational
electronic health records data. As the fairness criteria is ill-defined without
knowledge of the data generating process, we use a variational autoencoder to
perform counterfactual inference in the context of an assumed causal graph.
While our technique provides a means to trade off maintenance of fairness with
reduction in predictive performance in the context of a learned generative
model, further work is needed to assess the generality of this approach.
