100 citations · 207 across the 28 of their papers we have counts for
6 papers · 1 filter
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas +2
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-worl…
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Alessandro Bregoli, Karin Rathsman, Marco Scutari +2
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is importa…
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
Lorenzo Valleggi, Marco Scutari, Federico Mattia Stefanini
Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statisti…
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models…
fairml: A Statistician's Take on Fair Machine Learning Modelling
Marco Scutari
The adoption of machine learning in applications where it is crucial to ensure fairness and accountability has led to a large number of model proposals in the literature, largely f…
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are…