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 | 30 | 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 | 12 | 2023 |

Bayesian model selection of Gaussian directed acyclic graph structures F Castelletti International Statistical Review 88 (3), 752-775, 2020 | 12 | 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 | 12 | 2019 |

Bayesian graphical modeling for heterogeneous causal effects F Castelletti, G Consonni Statistics in Medicine 42 (1), 15-32, 2023 | 7 | 2023 |

Equivalence class selection of categorical graphical models F Castelletti, S Peluso Computational Statistics & Data Analysis 164, 107304, 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 | 5 | 2022 |

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 | 3 | 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 |

Bayesian inference of graph-based dependencies from mixed-type data C Galimberti, S Peluso, F Castelletti Journal of Multivariate Analysis, 105323, 2024 | | 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 | | 2024 |

Learning Bayesian networks: a copula approach for mixed-type data F Castelletti Psychometrika, 1-29, 2024 | | 2024 |

Bayesian sample size determination for causal discovery F Castelletti, G Consonni Statistical Science 39 (2), 305-321, 2024 | | 2024 |

Bayesian causal discovery from unknown general interventions A Mascaro, F Castelletti https://arxiv.org/abs/2312.00509, 2023 | | 2023 |