Simple SummaryCoronary artery disease is one of the major cardiovascular diseases associated with multiple inherited and environmental risk factors, and constitutes one of the leading causes of mortality globally. Due to the exceptionally high mortality rates and incidence of coronary artery disease, there exists a pivotal need for the development of methodological algorithms for the prediction of its complexity and severity. This work aimed to assess the robustness of the relationships of previously identified, important genetic risk factors affecting the Syntax Score, an index that evaluates the complexity of the disease. The analysis shows that these relationships were quite robust under mild interventions, whereas in the case of stronger interventions, they were more sensitive. In addition, it validated a set of risk factors capable to predict future cardiovascular events in individuals and specific populations. Thus, this work may contribute to the advancement of the data analytics and algorithm development relevant to coronary artery disease patient handling, therapeutic decisions, and predictions of severity and complexity of the disease.AbstractAn ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.