Work

A tutorial on bayesian networks for psychopathology researchers.

Year: 2022

Type: article

Abstract: 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 Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models 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 recommend Bayesian networks be investigated to gain causal insight in psychological data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Source: Psychological Methods

DOI: 10.1037/met0000479

Authors: Giovanni Briganti, Marco Scutari, Richard J. McNally

Institutions: Université Libre de Bruxelles, Harvard University, Dalle Molle Institute for Artificial Intelligence Research

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4225775378

FWCI: 12.95

Cites: 138

Cited by: 256

Related to: 10