ArXiv Preprint
Health disparities, or inequalities between different patient demographics,
are becoming crucial in medical decision-making, especially in Electronic
Health Record (EHR) predictive modeling. To ensure the fairness of sensitive
attributes, conventional studies mainly adopt calibration or re-weighting
methods to balance the performance on among different demographic groups.
However, we argue that these methods have some limitations. First, these
methods usually mean a trade-off between the model's performance and fairness.
Second, many methods completely attribute unfairness to the data collection
process, which lacks substantial evidence. In this paper, we provide an
empirical study to discover the possibility of using deconfounder to address
the disparity issue in healthcare. Our study can be summarized in two parts.
The first part is a pilot study demonstrating the exacerbation of disparity
when unobserved confounders exist. The second part proposed a novel framework,
Parity Medical Deconfounder (PriMeD), to deal with the disparity issue in
healthcare datasets. Inspired by the deconfounder theory, PriMeD adopts a
Conditional Variational Autoencoder (CVAE) to learn latent factors (substitute
confounders) for observational data, and extensive experiments are provided to
show its effectiveness.
Zheng Liu, Xiaohan Li, Philip Yu
2022-10-28
