A Controlled Study of Fairness Interventions for Temporal Graph Transformers on ICU Mortality Prediction

Temporal Graph Transformers (TGTS) have been proposed for prediction on electronic health records (EHRs), but it is unclear whether their graph architecture reduces demographic perfor- mance gaps or whether standard fairness mitiga- tion behaves differently on TGTS than on sequen- tial baselines. We present a controlled study on the MIMIC-IV ICU mortality task. We compare three TGT edge configurations against classical, sequential, and transformer baselines, and bench- mark two in-training fairness interventions (sam- ple reweighting, variance regularization) and three post-hoc interventions (Platt scaling, isotonic re- gression, per-subgroup threshold equalization). We find that (i) TGT graph structure alone does not eliminate subgroup AUROC gaps, but the choice of edge type matters: TGTFULL achieves the smallest race AUROC gap of any model under matched training; (ii) single-attribute reweighting reduces the targeted attribute’s gap but enlarges the gap on at least one other attribute in every model evaluated; and (iii) per-subgroup threshold equalization on top of Platt-scaled probabilities reduces the TPR gap from 0.20–0.23 → <0.03 on both LSTM and TGTFULL, while calibration alone leaves AUROC gaps largely unchanged or worse.