Comparing inference methods for causal mediation analysis with nominal mediators: A simulation and empirical study

In this study we advance causal mediation analysis for nominal mediators within a potential outcome framework. Through Monte Carlo simulations, we compared three inference methods for testing total natural indirect effects (TNIE) and pure natural indirect effects (PNIE): non-parametric bootstrapping, parametric resampling, and Bayesian estimation. Results showed that nominal mediation models yielded accurate estimates across conditions, with both maximum likelihood and Bayesian approaches performing well. All three inference methods maintained acceptable Type I error control and achieved adequate statistical power in larger samples, with comparable performance across approaches. We provide an empirical illustration using survey data on healthcare payment types and mental health help-seeking behavior to illustrate the model's utility. Findings suggest that researchers can reliably estimate and test nominal mediation effects using any of the three inference approaches, providing a robust methodological framework for investigating causal pathways involving categorical mediating variables in social, behavioral, and health sciences.