Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

翻译：从观察性数据中检验临床假设的因果推断面临诸多挑战，因为底层数据生成模型及相关因果图通常不可得。此外，观察性数据可能包含缺失值，这会干扰因果发现算法对因果图的恢复——这一关键问题在临床研究中常被忽视。本研究利用一项关于子宫内膜癌的多中心研究数据，分析不同缺失机制对恢复后因果图的影响。具体方法是通过扩展最先进的因果发现算法，在保证理论严谨性的前提下引入专家知识。我们与临床专家共同验证了恢复后的因果图，表明该方法可发现具有临床相关性的解决方案。最后，从临床决策视角出发，通过图形化分离验证因果路径，讨论了因果图的拟合优度及其一致性。