Why Prediction Is Not Enough: A Practical Introduction to Causal Inference for Clinical Decision Support

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 Artificial Intelligence has become increasingly effective at predicting clinical outcomes, but prediction alone cannot determine whether a treatment actually caused an observed benefit. This distinction between prediction and causation lies at the heart of modern Clinical Decision Support (CDS). This article introduces the fundamental concepts of causal inference using a synthetic clinical dataset developed for educational purposes. Through practical examples, it explains counterfactuals, treatment-selection bias, confounding, propensity scores, and Inverse Probability Weighting (IPW), illustrating how these methods improve estimation of the Average Treatment Effect (ATE). Rather than emphasizing programming or mathematical detail, the focus is on developing an intuitive understanding of why causal inference matters and how it forms the foundation for personalized, AI-assisted clinical decision making. This article is the first in a two-part series that progresses from causal inference to individualized treatment recommendations and practical Clinical Decision Support systems.