CauReL: Dynamic Counterfactual Learning for Precision Drug Repurposing in Alzheimer's Disease
Abstract
Abstract Alzheimer’s disease has few effective therapies, and decades of amyloid- and tau-focused trials have delivered only modest benefit with substantial toxicity. Drug repurposing using real-world data offers a faster and lower-risk route to new treatments, yet current approaches typically average effects across populations, model disease onset and progression separately, and provide little insight into which patients are most likely to benefit. We present CauReL, a dynamic counterfactual representation learning framework that enables transparent, patient specific estimation of treatment effects from large-scale electronic health records for precision drug repurposing in AD. CauReL first learns balanced latent representations of treated and untreated patients using Integral Probability Metric regularization, then jointly predicts two clinically linked outcomes, incident AD and time from mild cognitive impairment (MCI) to AD, to generate paired counterfactual outcomes for every individual. A counterfactual explanation module quantifies how clinical features shape benefit at the patient level, and uplift trees transform complex heterogeneity into simple, rule-based subgroups suitable for trial enrichment and clinical decision support.Using independent cohorts from OneFlorida + and All of Us, we screened outpatient prescriptions with at least 20 percent exposure among 28,605 individuals with mild cognitive impairment, of whom 4,990 progressed to Alzheimer’s disease. CauReL…
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