Autor: |
Zanga, Alessio, Bernasconi, Alice, Lucas, Peter J. F., Pijnenborg, Hanny, Reijnen, Casper, Scutari, Marco, Stella, Fabio |
Rok vydání: |
2023 |
Předmět: |
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Druh dokumentu: |
Working Paper |
Popis: |
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. |
Databáze: |
arXiv |
Externí odkaz: |
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