1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…
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…