Evaluating the impact of protective equipment on child injury severity in road traffic crashes: an explainable machine learning and counterfactual analysis approach

This study evaluated how correct use of child protective equipment (child restraint systems, seat belts, and helmets) influences predicted injury severity for children involved in police-reported road crashes. Data from 69,108 participants under 18 years were analyzed, covering occupant, vehicle, roadway, environmental, and protection factors. An XGBoost classifier achieved ROC AUC = 0.8186 with balanced accuracy, precision, and recall. SHAP interpretation identified seating position and participant type as the most influential predictors. Counterfactual simulations, assuming full compliance with protective-equipment use, showed improved predicted outcomes in 64 cases, while 15 worsened. Helmet non-use was the most frequent lapse. Consistent, correct use of protective devices significantly shifts predicted outcomes toward less severe injuries. The explainable machine-learning and counterfactual framework quantifies the benefits of compliance and provides actionable evidence for targeted education, enforcement, and vehicle-safety design. The approach can be extended to other vulnerable groups, including pregnant occupants.