Maximizing Predictive Performance for Small Subgroups: Functionally Adaptive Interaction Regularization (FAIR)

TL;DR

FAIR is a new linear modeling framework designed to improve predictive performance for small subgroups in healthcare data, balancing fairness, interpretability, and overall accuracy.

Contribution

It introduces a full linear interaction model with group-specific regularization and weighting, enhancing performance for small groups while maintaining interpretability.

Findings

Improves predictive accuracy for small subgroups.

Maintains model interpretability in healthcare settings.

Outperforms existing methods in numerical and health data experiments.

Abstract

In many healthcare settings, it is both critical to consider fairness when building analytical applications but also uniquely unacceptable to lower model performance for one group to match that of another (e.g. fairness cannot be achieved by lowering the diagnostic ability of a model for one group to match that of another and lose overall diagnostic power). Therefore a modeler needs to maximize model performance across groups as much as possible, often while maintaining a model's interpretability, which is a challenge for a number of reasons. In this paper we therefore suggest a new modeling framework, FAIR, to maximize performance across imbalanced groups, based on existing linear regression approaches already commonly used in healthcare settings. We propose a full linear interaction model between groups and all other covariates, paired with a weighting of samples by group size and independent regularization penalties for each group. This efficient approach overcomes many of the limitations in current approaches and manages to balance learning from other groups with tailoring prediction to the small focal group(s). FAIR has an added advantage in that it still allows for model interpretability in research and clinical settings. We demonstrate its usefulness with numerical and health data experiments.