The Challenge of Generating Causal Hypotheses Using Network Models

Statistical network models based on Pairwise Markov Random Fields (PMRFs) are popular tools for analyzing multivariate psychological data, in large part due to their perceived role generating insights into causal relationships: a practice known as discovery the modeling literature. However, since not presented tools, they play is poorly understood among empirical researchers. In this paper, we provide treatment of how PMRFs such Gaussian Graphical Model (GGM) work using Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) models. We describe key assumptions needed show equivalence class that networks identify from data. clarify four common misconceptions found literature relating skeletons; chains relationships; collider bias; cyclic