The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study

• URL: http://arxiv.org/abs/2305.10050v2
• Date: Fri, 3 Nov 2023 14:37:39 GMT
• Title: The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
• Authors: Alessio Zanga, Alice Bernasconi, Peter J.F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari, Fabio Stella
• Abstract summary: We use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions.
• Score: 1.173358409934101
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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