Causal Machine Learning for Personalized Surgical Decision-Making in Aortic Dissection: A National Fairness-Aware Clinical AI Framework.

BACKGROUND: Most machine learning models for aortic dissection predict mortality but do not address treatment-effect heterogeneity, fairness, or bedside implementation. We developed a causal-inference-informed framework integrating prediction, heterogeneous treatment-effect estimation, fairness auditing, and individualized risk-benefit profiling. METHODS: Using 2016-2022 HCUP NIS discharge data, we analyzed 45,112 aortic dissection hospitalizations. Five ML models, plus a weighted ensemble, were trained on 2016-2019 data (n=24,475) and temporally tested on 2020-2022 data (n=20,624). CausalForestDML estimated surgery-associated heterogeneous effects with specification-based robustness analyses. We audited fairness across race/ethnicity, insurance status, and income, and performed internal-external geographic validation. RESULTS: In-hospital mortality was 11.0% (n=4,940). The ensemble achieved an AUROC of 0.845, with stable COVID-era performance. Calibration varied substantially across models (Brier 0.076-0.161). Causal forest estimated heterogeneous surgery-associated treatment effects (interpreted as observational associations), with 39.5% of patients aged ≥80 years showing negative CATEs versus 18.5% aged 65-80; however, all ATE 95% CIs crossed zero. Dissection type-stratified analysis showed distinct patterns for Type A (ATE +0.4 pp) versus Type B (ATE +0.8 pp). Fairness was consistent across the three largest racial groups (AUROC 0.820-0.862). Model discrimination was preserved with race excluded (AUROC 0.834 vs. 0.836). Geographic validation preserved discrimination (0.832 ± 0.007). CONCLUSIONS: This framework supports risk stratification and hypothesis generation regarding treatment-effect heterogeneity, but not causal treatment recommendations. Clinical deployment requires prospective testing and independent external validation.