Machine learning-enhanced causal inference of surgical decisions and rehabilitation strategies in traumatic brain injury.

Traumatic Brain Injury (TBI) affects approximately 69 million people globally each year and leaves over 5 million with lasting disability, making it a leading cause of death and long-term impairment across all ages. Yet, most TBI research still relies on correlation-based regressions and basic propensity score methods, which are insufficient for addressing treatment-selection bias. This limitation underscores the need for modern causal-effect models to produce actionable evidence. This work applies a unified causal inference framework to quantify the impact of craniotomy, rehabilitation timing, and rehabilitation intensity on cognitive, functional, and quality-of-life outcomes in moderate-to-severe TBI. Our approach integrates outcome-adaptive LASSO for confounder selection, causal graph neural networks for structure discovery, inverse-probability weighting for average treatment effects (ATEs), and a causal-effect variational autoencoder to account for latent confounding. We analyzed data from 79,604 patients in the U.S. Traumatic Brain Injury Model Systems (TBIMS) database. Key treatments included craniotomy, very-early versus delayed rehabilitation start, and short versus long rehabilitation stays. Outcomes included discharge Functional Independence Measure (FIM) cognitive and motor scores, as well as follow-up assessments of productivity, social participation, and life-satisfaction. Results showed that craniotomy was causally associated with modest but statistically significant reductions in all five discharge FIM domains (average ATE ≈ -0.10 to -0.17 on 1-7 scales). Very-early rehabilitation initiation was linked to improvements in follow-up productivity and life satisfaction (ATE≈ +0.03 to +0.09 on 0-1 scales). Longer rehabilitation stays yielded the largest positive effects, enhancing both follow-up productivity and global FIM scores (ATE ≈ +0.08 to +0.24). All models achieved ≥90% accuracy in treatment assignment prediction, supporting the strength of confounder control and the robustness of the causal inferences.