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Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare.
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
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.
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task.
Weighted Positive Binary Decision Diagrams for Exact Probabilistic Inference
Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks.
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