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

2023/05/17 by Alessio Zanga, Alice Bernasconi, Zanga, Alessio +11 · 2 citations
Computer Science · Biochemistry, Genetics and Molecular Biology · Mathematics · #Bayesian Modeling and Causal Inference #Biomedical Text Mining and Ontologies #Advanced Causal Inference Techniques

paper · pdf · doi:10.48550/arxiv.2305.10050

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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• Causal Discovery on Higher-Order Interactions
• Towards Privacy-Aware Bayesian Networks: A Credal Approach

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