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Bayesian networks are widely used to learn and reason about the dependencestructure of discrete variables. However, they are only capable of formallyencoding symmetric conditional independence, which in practice is often toostrict to hold. Asymmetry-labeled DAGs have been recently proposed to bothextend the class of Bayesian networks by relaxing the symmetric assumption ofindependence and denote the type of dependence existing between the variablesof interest. Here, we introduce novel structural learning algorithms for thisclass of models which, whilst being efficient, allow for a straightforwardinterpretation of the underlying dependence structure. A comprehensivecomputational study highlights the efficiency of the algorithms. A real-worlddata application using data from the Fear of COVID-19 Scale collected in Italyshowcases their use in practice.

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear