LIFT - XAI: Leveraging Important Features in Treatment Effects to Inform Clinical Decision-Making via Explainable AI

Clinicians rely on evidence from randomized controlled trials (RCTs) to decide on medical treatments for patients. However, RCTs often lack the granularity needed to inform decisions for individual patients or specific clinical scenarios. Recent advances in machine learning, particularly conditional average treatment effect (CATE) modeling, offer a promising approach for estimating patient-specific treatment effects. Yet, their adoption in clinical practice remains limited because these models often function as “black boxes”, making it difficult to understand which features drive treatment effect heterogeneity among individuals. To overcome these barriers, we introduce LIFT-XAI, a robust and principled framework that interprets ensemble CATE models with Shapley values, thereby accurately identifying unique features and patient subgroups driving the treatment effects. We validate LIFT-XAI on real-world clinical data from four RCTs comprising over 42,000 patients. We demonstrate that LIFT-XAI uncovered fasting glucose as the primary factor explaining conflicting outcomes between two major blood pressure trials (SPRINT and ACCORD) and discovered age as a critical treatment modifier in a new clinical setting–a finding later validated by a subsequent RCT. By enabling such powerful cross-cohort analysis and the discovery of novel patient subgroups, LIFT-XAI advances precision medicine across diverse settings, from chronic disease management to acute care.