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

Bayesian networks (BNs) are probabilistic graphical models which concisely represent the dependence structure between discrete variables through a directed acyclic graph (DAG) [29, 41]. Any conditional independence between variables embedded in the model can be directly read from the underlying DAG through the so-called D-separation criterion [30]. However, in practical applications, it has been found that often the symmetric assumption of conditional independence is too restrictive and models graphically depicting asymmetric independence are needed. Various notions of asymmetric conditional independence have been since defined, including context-specific [4], partial [32] and local [7], and formal studies of their properties appeared [11, 12, 36, 43]. Although extensions of BNs embedding and representing asymmetric conditional independence have been defined [16, 22, 31, 33, 42], they often lose the intuitiveness associated to DAGs and no software is available for their use in practice.