Hypertension affects about 1.4 billion adults globally and is a major modifiable risk factor for cardiovascular disease. Although several first-line antihypertensive drug classes exist, randomized controlled trials typically report only average treatment effects (ATEs), which mask important variability in individual patient responses. As a result, clinical guidelines often assume a homogeneous patient population, leading to trial-and-error prescribing, delayed blood pressure control, and avoidable adverse effects. I argue that causal forest models combined with double machine learning (DML) enable reliable estimation of heterogeneous treatment effects (HTEs) from observational electronic health record data. These methods can approximate randomized trial validity while capturing clinically meaningful variation in treatment response across patients. Compared with traditional approaches, they are computationally feasible and better suited for individualized treatment assessment. Therefore, comparative effectiveness research in hypertension should move beyond ATE-focused analyses toward routine HTE estimation using causal machine learning. This shift would support more precise, data-driven prescribing and improve patient outcomes.
Clinical Intelligence Research Press