Estimating Causal Effects on Psychological Networks Using Item Response Theory

Network models in which each variable interacts with the others in a complex system have emerged as an important alternative to latent variable models in psychometric research. However, confirmatory methods for group network comparison are limited by practical constraints, such as the computational intractability of the Ising model in large networks. In this study, we demonstrate how to estimate causal effects on network state and strength when direct network estimation is not feasible by leveraging the mathematical equivalencies between the Ising model and item response theory (IRT) models. We demonstrate through simulation that a two-parameter logistic (2PL) explanatory IRT model can simultaneously recover causal effects on network state and strength. We first apply the method to a single empirical example of a vocabulary assessment from a content literacy intervention to demonstrate model building and interpretation strategies. We then replicate our approach with 72 empirical datasets from randomized controlled trials with item-level outcome data in education, economics, health, and related fields. Our results show that causal effects on network strength are both common and uncorrelated with effects on network state, suggesting that causal network models can provide new insight into the impact of interventions in the social and behavioral sciences.