Background:There is a need for novel methods to determine preventable causes of racial health disparities. This need has been met with the development of improved methods for mediation modeling. Current mediational analysis methods call for an evaluation of statistical interaction or effect modification between the investigated cause and mediator. For racial disparity, this approach facilitates the estimation of racially specific risks for infant mortality. However, current methods for evaluating multiple interacting mediators are inadequate. The first objective of the study was to compare Bayesian estimation of potential outcomes to other approaches to mediation analysis that included interaction. The second objective was to evaluate three potentially interacting mediators of racial disparity for infant mortality by modeling the large dataset from the National Natality Database using Bayesian estimation of potential outcomes.Methods.A random sample of observations from the 2003 National Natality Database was used to compare the currently promoted methods for mediation modeling. Racial disparity was modeled as a separate function for each of three potential mediators, (i) maternal smoking, (ii) low birth weight and (iii) teenage maternity. As a second objective, direct Bayesian estimation of potential outcomes modeled infant mortality as function of the interactions among the three mediators and race using the full National Natality Database for the years 2016 to 2018.Results:The counterfactual model was inaccurate in estimating the proportion of racial disparity that was attributable to either maternal smoking or teenage maternity. The counterfactual approach did not accurately estimate the probabilities defined by counterfactual definitions. The error was a result of modeling the excess relative risk instead of the risk probabilities. Bayesian approaches did estimate the probabilities of the counterfactual definitions. Results showed that 73% of the racial disparity for infant mortality was attributed to infants born with low birth weight.Conclusions.Bayesian estimation of potential outcomes could evaluate whether proposed public health programs would affect races differently and decisions could include consideration of the causal effect the program may have on racial disparity. The large contribution of low birth weight to racial disparity for infant mortality should be further investigated to identify preventable factors for low birth weight.