Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulinplasma glucose concentration, plasma glucose concentrationbody mass index (BMI), triceps skin fold thicknessBMI and age, diastolic blood pressureBMI, and number of times pregnantage. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.