| Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach F Castelletti, G Consonni, ML Della Vedova, S Peluso Bayesian Analysis 13 (4), 1235-1260, 2018 | 35 | 2018 |
| Bayesian learning of multiple directed networks from observational data F Castelletti, L La Rocca, S Peluso, FC Stingo, G Consonni Statistics in Medicine 39 (30), 4745-4766, 2020 | 21 | 2020 |
| Bayesian inference of causal effects from observational data in Gaussian graphical models F Castelletti, G Consonni Biometrics 77 (1), 136-149, 2021 | 19 | 2021 |
| Discovering causal structures in Bayesian Gaussian directed acyclic graph models F Castelletti, G Consonni Journal of the Royal Statistical Society Series A: Statistics in Society 183 …, 2020 | 17 | 2020 |
| Bayesian model selection of Gaussian directed acyclic graph structures F Castelletti International Statistical Review 88 (3), 752-775, 2020 | 14 | 2020 |
| Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways F Castelletti, G Consonni The Annals of Applied Statistics 13 (4), 2289-2311, 2019 | 13 | 2019 |
| Network structure learning under uncertain interventions F Castelletti, S Peluso Journal of the American Statistical Association 118 (543), 2117-2128, 2023 | 12 | 2023 |
| Bayesian graphical modeling for heterogeneous causal effects F Castelletti, G Consonni Statistics in Medicine 42 (1), 15-32, 2023 | 10 | 2023 |
| Equivalence class selection of categorical graphical models F Castelletti, S Peluso Computational Statistics & Data Analysis 164, 107304, 2021 | 8 | 2021 |
| Bayesian causal inference in probit graphical models F Castelletti, G Consonni Bayesian Analysis 16 (4), 1113-1137, 2021 | 7 | 2021 |
| Structural learning and estimation of joint causal effects among network-dependent variables F Castelletti, A Mascaro Statistical Methods & Applications, 2021 | 7 | 2021 |
| BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs F Castelletti, A Mascaro https://arxiv.org/abs/2201.12003, 2022 | 6 | 2022 |
| Bayesian learning of network structures from interventional experimental data F Castelletti, S Peluso Biometrika, 2023 | 4 | 2023 |
| Joint structure learning and causal effect estimation for categorical graphical models F Castelletti, G Consonni, ML Della Vedova Biometrics, 2024 | 3 | 2024 |
| Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems F Castelletti, F Niro, M Denti, D Tessera, A Pozzi IEEE Access, 2024 | 2 | 2024 |
| Learning Bayesian networks: a copula approach for mixed-type data F Castelletti Psychometrika, 1-29, 2024 | 2 | 2024 |
| Bayesian sample size determination for causal discovery F Castelletti, G Consonni Statistical Science 39 (2), 305-321, 2024 | 2 | 2024 |
| Supplement to “Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways.” F Castelletti, G Consonni DOI, 2019 | 1 | 2019 |
| Bayesian inference of graph-based dependencies from mixed-type data C Galimberti, S Peluso, F Castelletti Journal of Multivariate Analysis 203, 105323, 2024 | | 2024 |
| Bayesian nonparametric mixtures of categorical directed graphs for heterogeneous causal inference F Castelletti, L Ferrini arXiv preprint arXiv:2409.00453, 2024 | | 2024 |
