The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The significance of the current study is that it directly addresses the problem of uncertainty in propensity score models via the method of Bayesian model averaging (BMA). The usefulness of the proposed method is that it provides the investigator a way to incorporate prior knowledge regarding the relationship between the covariates and treatment selection (via the Kaplan and Chen, 2012 approach) while at the same time acknowledging model uncertainty via Bayesian model averaging. This paper provides a fully Bayesian MCMC methodology to obtain propensity score and treatment effect estimates, as well as R code to conduct such an analysis. Research design utilizes a combination of simulation studies and real data analysis. The simulation study examines the choice of parameter and model priors. The real data example examines a model relating full vs. part-day kindergarten attendance on achievement outcomes for first grade student using the ECLS-K. Preliminary findings suggest that the fully MCMC algorithm for Bayesian model averaging within the PSA framework provides accurate expected a posteriori estimates of the treatment effect. An appendix provides additional information on the two-step Bayesian PSA model, Bayesian model averaging, and computational considerations.