摘要Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical ef-fects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data sci-ence and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional fea-ture selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowl-edge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowl-edge map of"disease-syndrome-symptom-prescription-medicine"to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of ob-servational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into re-liable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data sci-ence methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.