Why Personalized Treatment Effects Matter: Building AI-Assisted Clinical Decision Support with a T-Learner

This document is a self-archived educational monograph published through Zenodo to provide a permanent, citable scholarly record. It has not undergone formal peer review. The content reflects the author's original work on causal machine learning, Clinical Decision Support (CDS), and the practical application of artificial intelligence using synthetic patient data. This archived version corresponds to the technical article originally published on prakashkota.com on July 5, 2026, and has been reformatted as a scholarly monograph for long-term preservation, citation, and academic dissemination. The accompanying web article provides a web-optimized reading experience, while this version serves as the archival record. The concepts presented are demonstrated through a live interactive Clinical Decision Support application available at models.mlpowersai.com. Companion articles and related resources are referenced within the document. Abstract Estimating the average effectiveness of a treatment across a patient population is an important first step in evidence-based medicine. However, physicians treat individual patients rather than populations, and the expected benefit of the same treatment may differ substantially from one patient to another. This article introduces the concept of the Individual Treatment Effect (ITE) and demonstrates how causal machine learning can be used to estimate personalized treatment benefits. Using a synthetic oncology dataset, a T‑learner architecture based on two Random Forest models is developed to estimate the expected clinical outcome under both treated and untreated scenarios for the same patient. The difference between these two predicted outcomes provides an estimate of the patient's individualized treatment benefit and forms the basis of a prototype Clinical Decision Support (CDS) system. The article also discusses model evaluation, interpretation, limitations, and the role of AI in supporting, rather than replacing, physician decision making. Together with Part 1, this article provides a practical introduction to causal inference and individualized treatment-effect estimation for healthcare professionals, engineers, students, and data scientists interested in AI-assisted Clinical Decision Support.