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Heart disease remains a leading cause of mortality worldwide, necessitating robust methods for its early detection and intervention. This study employs a comprehensive approach to identify and analyze critical features contributing to heart disease. Using a dataset of 270 patients, three well-known feature importance techniques—Boruta, Information Gain, and Lasso Regression—are applied to determine the top five features for heart disease detection. Following the identification of these key features, the g-computation method, a causal inference technique, is utilized to explore the causal relationships between these features and the presence of heart disease. The innovation of this research lies in providing valuable insights not only into the features that are highly correlated with chronic heart disease but also into those that have a direct causal impact on patient classification, using a well-known causal inference technique, g-estimation. This integrated approach enhances the understanding of heart disease etiology and can inform more effective diagnostic and therapeutic strategies.心脏疾病仍是全球范围内致死的首要病因，因此亟需稳健的方法实现其早期检测与干预。本研究采用综合研究框架，对与心脏疾病相关的关键特征展开识别与分析。本研究依托270名患者的数据集，运用三种经典特征重要性分析方法——博鲁塔（Boruta）算法、信息增益（Information Gain）以及套索回归（Lasso Regression）——筛选出用于心脏病检测的五大关键特征。在确定上述关键特征后，本研究借助因果推断技术中的g计算法（g-computation），探究这些特征与心脏病患病状态之间的因果关联。本研究的创新之处在于，借助经典因果推断技术g估计法（g-estimation），不仅揭示了与慢性心脏病高度相关的特征，还明确了对患者患病分类具有直接因果影响的特征。这种整合式研究范式有助于深化对心脏病病因学的认知，并可为更高效的诊断与治疗策略制定提供参考依据。