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Publications

Books

Old books and book editions are available here.

Book Chapters

Old book chapters are available here.

Refereed Journal Articles

In the works

• fairml: A Statistician's Take on Fair Machine Learning Modelling. [ arXiv (preprint) ]
  M. Scutari.
  Journal of Statistical Software.
• Introducing Bayesian Analysis for Clinicians: Gender-Associated Risk Assessment of Intracranial Aneurysms. [  ]
  P. Bijlenga, G. Spinner, M. Scutari, M. Delucchi and S. Hirsch.
  Proceedings of the 10th European-Japanese Cerebrovascular Congress.
• A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer. [ SSRN (preprint) ]
  A. Bernasconi, A. Zanga, P. J. F. Lucas, M. Scutari, S. Di Cosimo, M. C. De Santis, E. La Rocca, P. Baili, I. Cavallo, P. Verderio, C. M. Ciniselli, S. Pizzamiglio, A.Blanda, P. Perego, P. Vallerio, F. Stella, A. Trama and the Ada Working Group.
  Cancers.
• Abnormal Child Behavior in Primary School Students: a Bayesian Network Analysis. [  ]
  A. C. Till, T. R. Henry, M. Scutari, G. Briganti.
  Psychological Reports.
• Causal Modeling in Large-Scale Data to Improve Identification of Adults at Risk for Combined and Common Variable Immunodeficiencies. [ medRxiv (preprint) ]
  G. Papanastasiou, M. Scutari, R. Tachdjian, V. Hernandex-Trujillo, J. Raasch, K. Billmeyer, N. Vasilyev and V. Ivanov.
  NPJ Digital Medicine.
• Federated Causal Discovery with Missing Data in a Multicentric Study on Endometrial Cancer. [  ]
  A. Zanga, A. Bernasconi, P. J. F. Lucas, H. Pijnenborg, C. Reijnen, M. Scutari and A. C. Constantinou.
  Proceedings of the 3rd Workshop on Artificial Intelligence for Healthcare, 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024).

In print

• A Causal Network Model to Estimate the Cardiotoxic Effect of Oncological Treatments in Young Breast Cancer Survivors. [  ]
  A. Bernasconi, A. Zanga, P. J. F. Lucas, M. Scutari, A. Trama and F. Stella.
  Progress in Artificial Intelligence. This is an extended version of the “Towards a Transportable Causal Network Model Based on Observational Healthcare Data” AIxIA paper.
• Information Theory, Machine Learning, and Bayesian Networks in the Analysis of Dichotomous and Likert Responses for Questionnaire Psychometric Validation. [ PsyArXiv ]
  M. Orsoni, M. Benassi and M. Scutari.
  Psychological Methods.
• Network Analysis: An Overview for Mental Health Research. [ doi ]
  G. Briganti, M. Scutari, S. Epskamp, D. Borsboom, R. Hoekstra, H. F. Golino, A. P. Christensen, O. V. Ebrahimi, A. Heeren, R. van Bork, J. de Ron, L. Bringmann, K. Huth, A. Isvoranu, M. Marsman, T. Blanken, T. R. Henry, E. Fried and R. J. McNally.
  International Journal of Methods in Psychiatric Research.

Published

• Using Bayesian Networks to Investigate Psychological Constructs: The Case of Empathy. [ html | pdf | online supplementary material | doi ]
  G. Briganti, J. Decety, M. Scutari, R. J. McNally and P. Linkowski (2024).
  Psychological Reports, 127(5), 2334–2346.
• Inferring Skin-Brain-Skin Connections from Infodemiology Data using Dynamic Bayesian Networks. [ arXiv (preprint) | medRxiv (preprint) | html | pdf | doi | online supplementary material ]
  M. Scutari, D. Kerob and S. Salah (2024).
  Scientific Reports, 14, 10266.
• Learning Bayesian Networks with Heterogeneous Agronomic Datasets via Mixed-Effect Models and Hierarchical Clustering. [ arXiv (preprint) | html | pdf | doi ]
  L. Valleggi, M. Scutari and F. M. Stefanini (2024).
  Engineering Applications of Artificial Intelligence, 31, 107867.
• Entropies and Divergences for Bayesian Networks: Computational Complexity and Efficient Implementation. [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari (2024).
  Algorithms, 17(1), 24.
• Towards a Transportable Causal Network Model Based on Observational Healthcare Data. [ arXiv (preprint) | pdf ]
  A. Bernasconi, A. Zanga, P. J. F. Lucas, M. Scutari and F. Stella (2023).
  Proceedings of the 2nd Workshop on Artificial Intelligence for Healthcare, 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023), 67–82.
• Machine Learning Software and Pipelines. [ html | doi ]
  M. Scutari and M. Malvestio (2023).
  Wiley StatsRef: Statistics Reference Online.
• Developing and Running Machine Learning Software: Machine Learning Operations (MLOps). [ html | doi ]
  M. Scutari and M. Malvestio (2023).
  Wiley StatsRef: Statistics Reference Online.
• Analyzing Complex Systems with Cascades using Continuous Time Bayesian Networks. [ arXiv (preprint) | html | pdf | doi ]
  A. Bregoli, K. Rathsman, M. Scutari, F. Stella and S. W. Mogensen (2023).
  Proceedings of the 30th International Symposium on Temporal Representation and Reasoning, Leibniz International Proceedings in Informatics (LIPIcs), 8:1–8:21.
• A Tutorial on Bayesian Networks for Psychopathology Researchers. [ PsyArXiv (preprint) | html | doi ]
  G. Briganti, M. Scutari and R. J. McNally (2023).
  Psychological Methods, 28(4), 947–961.
• Causal Discovery with Missing Data in a Multicentric Clinical Study. [ arXiv (preprint) | html | doi ]
  A. Zanga, A. Bernasconi, P. J. F. Lucas, H. Pijnenborg, C. Rejinen, M. Scutari and F. Stella (2023).
  Proceedings of the 21st International Conference on Artificial Intelligence in Medicine, Lecture Notes in Artificial Intelligence, 40–44.
• Path Analysis Models Integrating Psychological, Neuro-Physiological and Clinical Variables in Individuals with Tension-Type Headache. [ html | doi ]
  B. X. W. Liew, M. Palacios-Ceña, M. Scutari, S. Fuensalida-Novo, A. Guerrero-Peral, C. Ordás-Bandera, J. A. Pareja and C. Fernández-de-las-Peñas (2023).
  The Journal of Pain, 24(3), 426–436.
• Data-Driven Network Analysis Identified Subgroup-Specific Low Back Pain Pathways: A Cross-Sectional GLA:D Back Study. [ html | pdf | doi ]
  B. X. W. Liew, J. Hartvigsen, M. Scutari and A. Kongsted (2023).
  Journal of Clinical Epidemiology, 153, 66–77.
• Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach. [ pdf ]
  A. Zanga, A. Bernasconi, P. Lucas, H. Pijnenborg, C. Reijnen, M. Scutari and F. Stella (2022).
  Proceedings of the 1st Workshop on Artificial Intelligence for Healthcare, 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022), 1–15.
• Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets. [ arXiv (preprint) | html | pdf ]
  M. Scutari, C. Marquis and L. Azzimonti (2022).
  Proceedings of Machine Learning Research 186 (PGM 2022), 73–84.
• Achieving Fairness with a Simple Ridge Penalty. [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari, F. Panero and M. Proissl (2022).
  Statistics and Computing, 32, 77.
• Comments on: "Hybrid Semiparametric Bayesian Networks". [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari (2022).
  TEST, 30, 328–330.
• Bayesian Network Analysis Reveals the Interplay of Intracranial Aneurysm Rupture Risk Factors. [ html | pdf | doi ]
  M. Delucchi, G. R. Spinner, M. Scutari, P. Bijlenga, S.Morel, C. M. Friedrich, R. Furrer and S. Hirsch (2022).
  Computers in Biology and Medicine, 147, 105740.
• Do Short-Term Effects Predict Long-Term Improvements in Women who Receive Manual Therapy or Surgery for Carpal Tunnel Syndrome? A Bayesian Network Analysis of a Randomized Clinical Trial. [ html | doi ]
  B. X. W. Liew, A. I. de-la-Llave-Rincón, M. Scutari, J. L. Arias-Buría, C. E. Cook, J. Cleland and C. Fernández-de-las-Peñas (2022).
  Physical Therapy, 102(4), pzac015.
• A Bayesian Hierarchical Score for Structure Learning from Related Data Sets. [ arXiv (preprint) | html | pdf | doi ]
  L. Azzimonti, G. Corani and M. Scutari (2021).
  International Journal of Approximate Reasoning, 142, 248–265. This is an extended version of the “Structure Learning with a Hierarchical Bayesian Score” PMLR paper.
• How Does Individualised Physiotherapy Work for People with Low Back Pain? A Bayesian Network Analysis Using Randomised Controlled Trial Data. [ html | pdf | doi ]
  B. X. W. Liew, J. J. Ford, M. Scutari and A. J. Hahne (2021).
  PLoS ONE, 16(10), 1–16.
• Learning Bayesian Networks from Incomplete Data with the Node-Averaged Likelihood. [ arXiv (preprint) | html | pdf | doi ]
  T. Bodewes and M. Scutari (2021).
  International Journal of Approximate Reasoning, 138, 145–160. This is an extended version of the “Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data” PMLR paper.
• A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks. [ arXiv (preprint) | html | pdf | doi ]
  A. Bregoli, M. Scutari and F. Stella (2021).
  International Journal of Approximate Reasoning, 138, 105–122. This is an extended version of the “Constraint-Based Learning for Continuous-Time Bayesian Networks” PMLR paper.
• Self-Efficacy Beliefs and Pain Catastrophizing Mediate Between Pain Intensity and Pain Interference in Whiplash-Associated Disorders. [ html | pdf | doi ]
  Y. Pedrero-Martin, J. Martinez-Calderon, D. Falla, B. X. W. Liew, M. Scutari and A. Luque-Suarez (2021).
  The Clinical Journal of Pain, 30, 1689–1698.
• Network Structures of Symptoms from the Zung Depression Scale. [ PsyArXiv (preprint) | html | pdf | online supplementary material | doi ]
  G. Briganti, M. Scutari and P. Linkowski (2021).
  Psychological Reports, 124(4), 1897–1911.
• Mechanisms of Recovery after Neck-Specific or General Exercises in Patients with Cervical Radiculopathy. [ html | pdf | doi ]
  B. X. W. Liew, A. Peolsson, D. Falla, J. A. Cleland, M. Scutari, M. Kierkegaard, Å. Dedering (2021).
  European Journal of Pain, 25(5), 1162–1172.
• Constraint-Based Learning for Continuous-Time Bayesian Networks. [ arXiv (preprint) | html | pdf ]
  A. Bregoli, M. Scutari and F. Stella (2020).
  Proceedings of Machine Learning Research, 138 (PGM 2020), 41–52.
• Structure Learning with a Hierarchical Bayesian Score. [ arXiv (preprint) | html | pdf ]
  L. Azzimonti, G. Corani and M. Scutari (2020).
  Proceedings of Machine Learning Research, 138 (PGM 2020), 5–16.
• Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data. [ arXiv (preprint) | html | pdf ]
  T. Bodewes and M. Scutari (2020).
  Proceedings of Machine Learning Research, 138 (PGM 2020), 29–40.
• Hard and Soft EM in Bayesian Network Learning from Incomplete Data. [ arXiv (preprint) | html | pdf | doi ]
  A. Ruggieri, F. Stranieri, F. Stella and M. Scutari (2020).
  Algorithms, 13(12):329, 1–16.
• An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes. [ html | pdf | doi ]
  H. Fisher, M. Gittoes, L. Evans, L. Bitchell, R. Mullen and M. Scutari (2020).
  Frontiers in Sports and Active Living, 2(595619), 1–20.
• A Machine Learning Approach to Relationships Among Alexithymia Components. [ pdf ]
  G. Briganti, M. Scutari and P. Linkowski (2020).
  Psychiatria Danubina, 32(Suppl. 1), 180–187.
• Tectonic Control on Global Variations in the Record of Large-Magnitude Explosive Eruptions in Volcanic Arcs. [ html | pdf | doi ]
  T. E. Sheldrake, L. Caricchi and M. Scutari (2020).
  Frontiers in Earth Science, 8:127, 1–14.
• Bayesian Network Models for Incomplete and Dynamic Data. [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari (2020).
  Statistica Neerlandica, 74(3), 397–419.
• Probing the Mechanisms Underpinning Recovery in Post-Surgical Patients with Cervical Radiculopathy Using Bayesian Networks. [ html | pdf | doi ]
  B. X. W. Liew, A. Peolsson, M. Scutari, H. Löfgren, J. Wibault, Å. Dedering, B. Öberg, P. Zsigmond and D. Falla (2020).
  European Journal of Pain, 24(5), 909–920.
• Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms. [ arXiv (preprint) | html | pdf | online supplementary material | doi ]
  M. Scutari, C. E. Graafland and J. M. Gutiérrez (2019).
  International Journal of Approximate Reasoning, 115, 235–253. This is an extended version of the “Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms?” PMLR paper.
• Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash Associated Disorders using Bayesian Networks. [ html | doi ]
  B. X. W. Liew, M. Scutari, A. Peolsson, G. Peterson, M. L. Ludvigsson and D. Falla (2019).
  The Clinical Journal of Pain, 35(8), 647–655.
• Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation. [ arXiv (preprint) | html | pdf | online supplementary material | doi ]
  M. Scutari, C. Vitolo and A. Tucker (2019).
  Statistics and Computing, 29(5), 1095–1108.

Older papers from 2015 to 2018 are available here.

Older papers from up to 2014 are available here.

Ph.D. Dissertation and Technical Reports

The material from my Ph.D. dissertation is available here.

Teaching Material

My teaching material is available here.

Invited Talks, (Short) Course Slides, Conference Presentations and Posters

• Causal Modelling in Space and Time. [ pdf ]
  Department of Systems Innovation, Graduate School of Engineering Science, Osaka University; and online for the Behaviormetric Society (June 18, 2024). And again at the Institute of Medical Science, The University of Tokyo (July 8, 2024). And again at the Data Science and AI Innovation Research Promotion Center, Faculty of Data Science, Shiga University (July 23, 2024). And again at the Brazilian Symposium on Probability and Statistics, Fortaleza (August 5, 2024). And again at a Mathematics webinar organised by MDPI/Sciforum (October 10, 2024).

• Distilling Causal Models: Model Averaging, Federated Learning and More. [ pdf ]
  AI and Machine Learning Summer School, Cambridge Centre for AI in Medicine (CCAIM), online (September 2–6, 2024).
• bnlearn, Learning Bayesian Networks 15 Years Later. [ pdf ]
  Bayesian Networks Modelling Association (BNMA), online (June 24, 2024).
• Fairness in Machine Learning. [ pdf ]
  “Data Science for the Sciences” Conference, Bern (April 11–12, 2024).
• Different Takes on the Causal Modelling of Spatio-Temporal Data. [ pdf ]
  Department of Informatics, Systems and Communication, University of Milano Bicocca (March 6, 2024). And again at the Florence Center for Data Science and the Department of Statistics, Informatics and Applications, University of Florence (May 24, 2024).
• Network Structures for Psychological Constructs: The Case of Empathy. [ pdf ]
  University Center for Statistics in the Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, Milan (January 29, 2024).
• The Anatomy of a Machine Learning Pipeline. [ pdf | online material ]
  European Network for Business and Industrial Statistics (ENBIS), online (January 10, 2024). And again at the University of Milano Bicocca (September 16, 2024).
• Fair Machine Learning: Achieving Fairness with a Simple Ridge Penalty. [ pdf ]
  Department of Statistics, London School of Economics (June 12, 2023). And again at the Department of Informatics, Systems and Communication, University of Milano Bicocca (June 29, 2023).
• Analysing Google Search Trends Data with Dynamic Bayesian Networks. [ pdf ]
  Institute for Global Environmental Strategies (IGES), Tokyo (January 20, 2023). And again at the Department of Systems Innovation, Graduate School of Engineering Science, Osaka University (January 30, 2023). And again at the Bernoulli Workshop on Sparse Inference on Complex Networks, Università della Svizzera Italiana (June 27, 2023).
• Bayesian Network Models for Continuous-Time and Structured Data. [ pdf ]
  National Institute of Advanced Industrial Science and Technology (AIST), Tokyo (January 19, 2023).

Older talks from 2019 to 2022 are available here.

Older talks from 2015 to 2018 are available here.

Older talks from up to 2014 are available here.