5 citations · 7 across the 8 of their papers we have counts for
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stat.ME2023★ 5 cited
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…
stat.ME2023
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…
stat.ME2023
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…