A Tutorial on Bayesian Networks for Psychopathology Researchers

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce BayesianNetworks to identify admissible causal relationships in cross-sectional data, as well as how to estimate thesemodels in R through three algorithm families with an empirical example data set of depressive symptoms.In addition, we discuss common problems and questions related to Bayesian networks. We recommendBayesian networks be investigated to gain causal insight in psychological data.Translational AbstractIn the last decade, the network framework for the study of mental disorders has emerged as a new wayof investigating mental disorders as issuing from interactions among their constituent symptoms.Network analysis is the statistical aspect of this framework, as researchers use nodes (symptoms) andedges (connections between symptoms) to model disorders: Usually, network structures encode pairwiseinteractions among symptoms. In this study, we introduce Bayesian networks, models that can identifyadmissible causal relationships in cross-sectional data, as well as a tutorial for applied researchers onhow to estimate those models in R. In addition, we discuss common problems and questions related toBayesian network models.SCOPUS: ar.j