| 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 | 29 | 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 | 18 | 2020 |
| Bayesian inference of causal effects from observational data in Gaussian graphical models F Castelletti, G Consonni Biometrics 77 (1), 136-149, 2021 | 14 | 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 | 13 | 2020 |
| Network structure learning under uncertain interventions F Castelletti, S Peluso Journal of the American Statistical Association 118 (543), 2117-2128, 2023 | 11 | 2023 |
| Bayesian model selection of Gaussian directed acyclic graph structures F Castelletti International Statistical Review 88 (3), 752-775, 2020 | 11 | 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 | 11 | 2019 |
| Bayesian graphical modeling for heterogeneous causal effects F Castelletti, G Consonni Statistics in Medicine 42 (1), 15-32, 2023 | 7 | 2023 |
| BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs F Castelletti, A Mascaro https://arxiv.org/abs/2201.12003, 2022 | 5 | 2022 |
| Equivalence class selection of categorical graphical models F Castelletti, S Peluso Computational Statistics & Data Analysis 164, 107304, 2021 | 5 | 2021 |
| Bayesian causal inference in probit graphical models F Castelletti, G Consonni Bayesian Analysis 16 (4), 1113-1137, 2021 | 5 | 2021 |
| Structural learning and estimation of joint causal effects among network-dependent variables F Castelletti, A Mascaro Statistical Methods & Applications, 2021 | 5 | 2021 |
| Bayesian learning of network structures from interventional experimental data F Castelletti, S Peluso Biometrika, 2023 | 2 | 2023 |
| Bayesian sample size determination for causal discovery F Castelletti, G Consonni https://arxiv.org/abs/2206.00755, 2022 | 1 | 2022 |
| 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 |
| Learning Bayesian networks: a copula approach for mixed-type data F Castelletti https://arxiv.org/abs/2312.13168, 2023 | | 2023 |
| Bayesian causal discovery from unknown general interventions A Mascaro, F Castelletti https://arxiv.org/abs/2312.00509, 2023 | | 2023 |
| Joint structure learning and causal effect estimation for categorical graphical models F Castelletti, G Consonni, ML Della Vedova https://arxiv.org/abs/2306.16068, 2023 | | 2023 |
| Multiple arrows in the Bayesian quiver: Bayesian learning of partially directed structures from heterogeneous data. L La Rocca, F Castelletti, S Peluso, FC Stingo, G Consonni Book of the Short Papers SIS 2022, 838-843, 2022 | | 2022 |
| Bayesian Multivariate Analysis of Mixed Data C Galimberti, F Castelletti, S Peluso Scientific Meeting of the Classification and Data Analysis Group of the …, 2021 | | 2021 |
