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. Intervento presentato a: Workshop: Sparse Inference on Complex Networks, Lugano, Svizzera.

Causal Discovery with Missing Data in a Multicentric Clinical Study

Zanga, A;Bernasconi, A;Stella, F
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.
abstract + slide
Causal discovery, Causal graphs, Missing data
English
Workshop: Sparse Inference on Complex Networks
2023
2023
none
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. Intervento presentato a: Workshop: Sparse Inference on Complex Networks, Lugano, Svizzera.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/444758
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