Given the global prevalence and high mortality of coronary artery disease (CAD), automated CAD diagnosis should evolve toward personalized methods to maximize its clinical value. However, existing techniques have been limited to artery-level prediction, lacking patient-level causality and failing to effectively account for individual patient confounders. In this work, for the first time, we introduce a Causal-Holistic Adaptive Intervention Network (CAIN) that tailors personalized CAD diagnosis for individual patients. CAIN generates semantic representations at both the patient and artery dual-levels for each case, constructing a holistic causal graph that captures individual-specific characteristics. It then implements adaptive causal intervention based on the patient’s specific condition, using dynamically updated and differentiated intervention variables. Experimental results on CCTA scans from 602 patients and 6,830 coronary branches across three clinical centers show that CAIN outperforms state-of-the-art methods, offering personalized clinical guidance. The source code is available at ( https://github.com/PerceptionComputingLab/CAIN ).