GapTrace: Controlled Attribution of Prediction Disparities in Clinical Time Series

Machine learning is increasingly used in clinical decision-making, raising concerns about both fairness and interpretability. These are usually studied separately: attribution methods explain which features influence predictions, while fairness analyses measure disparities without identifying the model behavior associated with them. We introduce GapTrace, a framework for auditing prediction disparities in time series models. Given a gap in predicted risk between patient groups, GapTrace measures how much of the gap remains after adjustment for correlated attributes, identifies features or feature clusters used differently across groups with false-discovery-rate control, and tests whether intervening on the selected features reduces the gap more than matched control interventions. GapTrace is agnostic to the attribution method and can be applied on top of existing tools. On synthetic and semi-synthetic datasets with known subgroup-specific mechanisms, GapTrace identifies the planted features (F1 up to 0.92, compared with 0.35 for random selection). On 30-day mortality prediction in MIMIC-IV, we find three different patterns. The largest prediction gap is across age groups (29.7 percentage points), with 92% remaining after adjustment for other demographics. GapTrace identifies 35 of 525 feature clusters with significant differences in attribution across age groups. Intervention on the selected features reduces the gap more than matched controls. In contrast, most of the marital-status gap disappears after adjustment, while the remaining insurance gap cannot be consistently attributed to a specific set of features. GapTrace turns a measured disparity into a testable explanation: which inputs the model relies on differently across groups, and whether they account for the gap, delivering fairness analysis and explainability together.