FLAT-WHITE: from target trial emulation to federated high-throughput causal machine learning using decentralized real-world data

Randomized clinical trials remain the gold standard for evaluating treatment effects but are often infeasible for rapidly assessing drug repurposing opportunities, particularly in complex conditions such as Alzheimer's disease. Target trial emulation (TTE) provides a principled framework for causal inference with electronic health records and, when coupled with modern machine learning, enables scalable analyses across distributed health systems. We present FLAT-WHITE (Federated Lossless Algorithm for Target Trial Emulation with Overlap Weighting for Healthcare Data Integration via Trusted and Efficient Communication), a novel federated causal machine learning pipeline for high-throughput treatment effect estimation without sharing patient-level data. FLAT-WHITE is a one-shot, lossless algorithm, requiring a single communication round while achieving results equivalent to centralized pooled analyses. With overlap weighting on the propensity score, it robustly addresses heterogeneity in treatment assignment and ensures valid causal inference in decentralized settings. We demonstrate FLAT-WHITE in a Alzheimer's drug repurposing study, scanning 181 candidate drugs using data from four health learning systems encompassing approximately 150,000 patients with mild cognitive impairment. Findings were validated externally in 339,180 patients from the TriNetX global research network. FLAT-WHITE enables rapid, large-scale, and secure evaluation of treatment effects and establishes a foundation for collaborative clinical research across diverse healthcare systems.