NATbox: a network analysis toolbox in R. - Shweta S. Chavan; Michael A. Bauer 002; Marco Scutari; Radhakrishnan Nagarajan (BMC Bioinform., 2009)

Identifying significant edges in graphical models of molecular networks. - Marco Scutari; Radhakrishnan Nagarajan (Artif. Intell. Medicine, 2013)

Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package. - Marco Scutari (CoRR, 2014)

Personalised Medicine: Taking a New Look at the Patient. - Marco Scutari (Foundations of Biomedical Knowledge Representation, 2015)

Graphical Modelling in Genetics and Systems Biology. - Marco Scutari (Foundations of Biomedical Knowledge Representation, 2015)

An Empirical-Bayes Score for Discrete Bayesian Networks. - Marco Scutari (Probabilistic Graphical Models, 2016)

Dirichlet Bayesian Network Scores and the Maximum Entropy Principle. - Marco Scutari (AMBN, 2017)

Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms? - Marco Scutari; Catharina Elisabeth Graafland; José Manuel Gutiérrez (PGM, 2018)

Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms. - Marco Scutari; Catharina Elisabeth Graafland; José Manuel Gutiérrez (Int. J. Approx. Reason., 2019)

Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation. - Marco Scutari; Claudia Vitolo; Allan Tucker (Stat. Comput., 2019)

Structure Learning from Related Data Sets with a Hierarchical Bayesian Score. - Laura Azzimonti; Giorgio Corani; Marco Scutari (PGM, 2020)

Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data. - Tjebbe Bodewes; Marco Scutari (PGM, 2020)

Constraing-Based Learning for Continous-Time Bayesian Networks. - Alessandro Bregoli; Marco Scutari; Fabio Stella (PGM, 2020)

Hard and Soft EM in Bayesian Network Learning from Incomplete Data. - Andrea Ruggieri; Francesco Stranieri; Fabio Stella; Marco Scutari (Algorithms, 2020)

Constraint-Based Learning for Continuous-Time Bayesian Networks. - Alessandro Bregoli; Marco Scutari; Fabio Stella (CoRR, 2020)

Achieving Fairness with a Simple Ridge Penalty. - Marco Scutari; Manuel Proissl (CoRR, 2021)

Learning Bayesian networks from incomplete data with the node-average likelihood. - Tjebbe Bodewes; Marco Scutari (Int. J. Approx. Reason., 2021)

A constraint-based algorithm for the structural learning of continuous-time Bayesian networks. - Alessandro Bregoli; Marco Scutari; Fabio Stella (Int. J. Approx. Reason., 2021)

Risk Assessment of Lymph Node Metastasis in Endometrial Cancer Patients: A Causal Approach. - Alessio Zanga; Alice Bernasconi; Peter J. F. Lucas; Hanny Pijnenborg; Casper Reijnen; Marco Scutari; Fabio Stella (HC@AIxIA, 2022)

Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets. - Marco Scutari; Christopher Marquis; Laura Azzimonti (PGM, 2022)

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. - Matteo Delucchi; Georg Ralph Spinner; Marco Scutari; Philippe Bijlenga; Sandrine Morel; Christoph M. Friedrich; Reinhard Furrer; Sven Hirsch (Comput. Biol. Medicine, 2022)

Comments on: "Hybrid Semiparametric Bayesian Networks". - Marco Scutari (CoRR, 2022)

Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets. - Marco Scutari; Christopher Marquis; Laura Azzimonti (CoRR, 2022)

A Bayesian hierarchical score for structure learning from related data sets. - Laura Azzimonti; Giorgio Corani; Marco Scutari (Int. J. Approx. Reason., 2022)

Achieving fairness with a simple ridge penalty. - Marco Scutari; Francesca Panero; Manuel Proissl (Stat. Comput., 2022)

Towards a Transportable Causal Network Model Based on Observational Healthcare Data. - Alice Bernasconi; Alessio Zanga; Peter J. F. Lucas; Marco Scutari; Fabio Antonio Stella (HC@AIxIA, 2023)

Causal Discovery with Missing Data in a Multicentric Clinical Study. - Alessio Zanga; Alice Bernasconi; Peter J. F. Lucas; Hanny Pijnenborg; Casper Reijnen; Marco Scutari; Fabio Stella (AIME, 2023)

Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks. - Alessandro Bregoli; Karin Rathsman; Marco Scutari; Fabio Stella; Søren Wengel Mogensen (TIME, 2023)

fairml: A Statistician's Take on Fair Machine Learning Modelling. - Marco Scutari (CoRR, 2023)

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach. - Alessio Zanga; Alice Bernasconi; Peter J. F. Lucas; Hanny Pijnenborg; Casper Reijnen; Marco Scutari; Fabio Stella (CoRR, 2023)

Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. - Marco Scutari (Algorithms, 2024)

Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering. - Lorenzo Valleggi; Marco Scutari; Federico Mattia Stefanini (Eng. Appl. Artif. Intell., 2024)

Unveiling Sleep Dysregulation in Chronic Fatigue Syndrome with and Without Fibromyalgia Through Bayesian Networks. - Michal Bechny; Marco Scutari; Julia van der Meer; Francesca D. Faraci; Stéphane Meystre; Benjamin H. Natelson; Akifumi Kishi (AIME (1), 2025)

Causal Networks of Infodemiological Data: Modelling Dermatitis. - Marco Scutari; Samir Salah; Delphine Kerob; Jean Krutmann (AIME (1), 2025)

Federated causal discovery with missing data in a multicentric study on endometrial cancer. - Alessio Zanga; Alice Bernasconi; Peter J. F. Lucas; Hanny Pijnenborg; Casper Reijnen; Marco Scutari; Anthony C. Constantinou (J. Biomed. Informatics, 2025)

Large scale causal modeling to identify adults at risk for combined and common variable immunodeficiencies. - Giorgos Papanastasiou; Marco Scutari; Raffi Tachdjian; Vivian Hernandez-Trujillo; Jason Raasch; Kaylyn Billmeyer; Nikolay V. Vasilyev; Vladimir Ivanov (npj Digit. Medicine, 2025)

Benchmarking Constraint-Based Bayesian Structure Learning Algorithms: Role of Network Topology. - Radha Nagarajan; Marco Scutari (CoRR, 2025)

Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics. - Daniele Malpetti; Marco Scutari; Francesco Gualdi; Jessica van Setten; Sander W. Van der Laan; Saskia Haitjema; Aaron Mark Lee; Isabelle Hering; Francesca Mangili (CoRR, 2025)