Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being. This study undertakes a comprehensive exploration of various structural learning algorithms to discern causal pathways amongst potential risk factors influencing diabetes progression. The methodology involves the application of these algorithms to relevant diabetes data, followed by the conversion of their output graphs into Causal Bayesian Networks (CBNs), enabling predictive analysis and the evaluation of discrepancies in the effect of hypothetical interventions within our context-specific case study. This study highlights the substantial impact of algorithm selection on intervention outcomes. To consolidate insights from diverse algorithms, we employ a model-averaging technique that helps us obtain a unique causal model for diabetes derived from a varied set of structural learning algorithms. We also investigate how each of those individual graphs, as well as the average graph, compare to the structures elicited by a domain expert who categorised graph edges into high confidence, moderate, and low confidence types, leading into three individual graphs corresponding to the three levels of confidence. The resulting causal model and data are made available online, and serve as a valuable resource and a guide for informed decision-making by healthcare practitioners, offering a comprehensive understanding of the interactions between relevant risk factors and the effect of hypothetical interventions. Therefore, this research not only contributes to the academic discussion on diabetes, but also provides practical guidance for healthcare professionals in developing efficient intervention and risk management strategies.

翻译：糖尿病作为一种普遍且长期的健康挑战，对全球健康、医疗金融体系及社会福祉产生了重大影响。本研究系统性地探索了多种结构学习算法，以识别影响糖尿病进展的潜在风险因素间的因果路径。研究方法包括：将上述算法应用于相关糖尿病数据，随后将其输出图转化为因果贝叶斯网络（CBNs），从而在本研究特定案例中实现预测分析，并评估假设性干预效果的差异。本研究凸显了算法选择对干预结果的显著影响。为综合不同算法的见解，我们采用模型平均技术，从多样化的结构学习算法中获取糖尿病特有的因果模型。我们进一步探讨了各单一图结构及平均图结构与领域专家所构建图结构之间的差异——专家依据置信度将图边分类为高、中、低三类，生成了对应三个置信水平的三组独立图。最终的因果模型及数据已在线公开，可为医疗从业者的知情决策提供宝贵资源和指导，有助于全面理解相关风险因素的交互作用及假设性干预效果。因此，本研究不仅丰富了糖尿病的学术讨论，还为医疗专业人员制定高效干预与风险管理策略提供了实践指引。