5 citations · 7 across the 8 of their papers we have counts for
10 papers
Graphical model-based clustering of categorical data
Laura Ferrini, Federico Castelletti
Clustering multivariate data is a pervasive task in many applied problems, particularly in social studies and life science. Model-based approaches to clustering rely on mixture mod…
Bayesian nonparametric mixtures of categorical directed graphs for heterogeneous causal inference
Federico Castelletti, Laura Ferrini
Quantifying causal effects of exposures on outcomes, such as a treatment and a disease respectively, is a crucial issue in medical science for the administration of effective thera…
Learning Bayesian networks: a copula approach for mixed-type data
Federico Castelletti
Estimating dependence relationships between variables is a crucial issue in many applied domains, such as medicine, social sciences and psychology. When several variables are enter…
Bayesian causal discovery from unknown general interventions
Alessandro Mascaro, Federico Castelletti
We consider the problem of learning causal Directed Acyclic Graphs (DAGs) using combinations of observational and interventional experimental data. Current methods tailored to this…
Joint structure learning and causal effect estimation for categorical graphical models
Federico Castelletti, Guido Consonni, Marco Luigi Della Vedova
We consider a a collection of categorical random variables. Of special interest is the causal effect on an outcome variable following an intervention on another variable. Condition…
Bayesian sample size determination for causal discovery
Federico Castelletti, Guido Consonni
Graphical models based on Directed Acyclic Graphs (DAGs) are widely used to answer causal questions across a variety of scientific and social disciplines. However, observational da…
BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs
Federico Castelletti, Alessandro Mascaro
Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, the…
Bayesian graphical modeling for heterogeneous causal effects
Federico Castelletti, Guido Consonni
Our motivation stems from current medical research aiming at personalized treatment using a molecular-based approach. The broad goal is to develop a more precise and targeted decis…
Equivalence class selection of categorical graphical models
Federico Castelletti, Stefano Peluso
Learning the structure of dependence relations between variables is a pervasive issue in the statistical literature. A directed acyclic graph (DAG) can represent a set of condition…
Bayesian causal inference in probit graphical models
Federico Castelletti, Guido Consonni
We consider a binary response which is potentially affected by a set of continuous variables. Of special interest is the causal effect on the response due to an intervention on a s…