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.
CITATION STYLE
Zanga, A., Bernasconi, A., Lucas, P. J. F., Pijnenborg, H., Reijnen, C., Scutari, M., & Stella, F. (2023). Causal Discovery with Missing Data in a Multicentric Clinical Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13897 LNAI, pp. 40–44). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34344-5_5
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