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.

Zanga, A., Bernasconi, A., Lucas, P., Pijnenborg, H., Reijnen, C., Scutari, M., et al. (2023). Causal Discovery with Missing Data in a Multicentric Clinical Study. In Artificial Intelligence in Medicine 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (pp.40-44). Springer [10.1007/978-3-031-34344-5_5].

Causal Discovery with Missing Data in a Multicentric Clinical Study

Zanga A.
Primo
;
Bernasconi A.
Secondo
;
Stella F.
Ultimo
2023

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.
paper
Causal discovery; Causal graphs; Missing data;
English
21st International Conference on Artificial Intelligence in Medicine, AIME 2023 - 12 June 2023 through 15 June 2023
2023
Juarez, JM; Marcos, M; Stiglic, G; Tucker, A
Artificial Intelligence in Medicine 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings
9783031343438
2023
13897LNCS
40
44
reserved
Zanga, A., Bernasconi, A., Lucas, P., Pijnenborg, H., Reijnen, C., Scutari, M., et al. (2023). Causal Discovery with Missing Data in a Multicentric Clinical Study. In Artificial Intelligence in Medicine 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (pp.40-44). Springer [10.1007/978-3-031-34344-5_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/419859
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