The AIC model selection method applied to path analytic models compared using a d-separation test

Classical path analysis is a statistical technique used to test causal hypotheses involving multiple variables without latent variables, assuming linearity, multivariate normality, and a sufficient sample size. The d-separation (d-sep) test is a generalization of path analysis that relaxes these assumptions. Although model selection using Akaike's information criterion (AIC) is well established for classical path analysis, this model selection technique has not yet been developed for d-sep tests. In this paper, I explain how to use the AIC statistic for d-sep tests, give a worked example, and include instructions (supplemental material) to implement the analysis in the R computing language.