Federated Propensity Score Matching for Bias Correction in ICU

Multi-site medical studies require methods that address confounding bias while respecting data privacy regulations. We propose a federated learning method integrating propensity score matching (PSM) to achieve both objectives simultaneously. Using data from five intensive care unit (ICU) databases (N=160,752), we evaluated within-hospital and cross-hospital PSM strategies within federated XGBoost. Cross-hospital PSM achieved 76.3% mean bias reduction (standardized mean difference (SMD): 0.070), while within-hospital PSM achieved 74.3% reduction (SMD: 0.074), compared to baseline SMD of 0.316. Both strategies maintained predictive performance (AUROC: 0.73-0.75). Our method enables rigorous causal inference in distributed healthcare networks without centralizing sensitive patient data.