Harnessing the power of virtual (digital) twins: Graphical causal tools for understanding patient and hospital differences

Traditional methods for evaluating hospital performance, such as regression or propensity score analysis, offer population-level comparisons but lack the granularity required for patient-level insight. We propose a causal framework based on virtual (digital) twins, enabling counterfactual outcome comparisons for individual patients across hospitals. Using data from the American Association for Thoracic Surgery (AATS) Quality Gateway Adult Cardiac Database, which includes 52,792 surgeries across 19 hospitals, we estimate patient-level causal effects for adverse surgical outcomes. Our approach combines model-free variable priority screening, random forests quantile classification (RFQ) for handling rare events, and isolation forests to assess treatment overlap and exclude invalid counterfactuals. Building on prior work, we introduce graphical tools for overlap diagnostics and counterfactual visualization at both the institutional and patient level. These tools reframe outcome modeling as individualized causal inference and support transparent, patient-centered hospital benchmarking.