Three approaches for conditional independence testing: An introduction and application within psychopathology research

In network psychometrics, constructs are oftentimes argued to map onto causal structures. Researchers have developed the graphical approach to causal inference as a formal framework in which causal relationships are represented as directed acyclic graphs (DAGs). It is difficult to model the directed, causal structures from correlational data; conditional dependencies and independencies are key to identifying DAGs that are consistent with observed data. The Bayesian approach provides three main methods that can test for conditional independence within graphical models: the credible interval, the Bayes factor, and the Bayesian model-averaged inclusion Bayes factor. In this talk, we will provide an introduction to these three approaches, highlight their strengths and limitations, and discuss a small-scale simulation study comparing the performance of these methods. Using the Bayesian model-averaging approach, we introduce the edge evidence plot for network psychometrics. The edge evidence plot visualizes the conditional (in)dependence relationship between variables. Its use will be illustrated with an example in the field of psychopathology. As such, in this last talk of the symposium, we aim to highlight the benefits of adopting the Bayesian approach to network analysis for applied researchers.