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Abstract: Inferring the causal relationship between a treatment and a response is complicated in non-randomized studies owing to the effects of potentially confounding variables. Extensive work has been conducted to account for the effects of such variables in statistical analyses. However, previous works have demonstrated that misspecifying the set of potential confounders can have significant consequences for causal effect estimation. Bayesian Adjustment for Confounders (BAC) is a Bayesian approach to variable selection, whereby a mixture of posteriors is used to combine the causal effect estimates from each model corresponding to a combination of the potential confounders. Our work uses Monte Carlo simulation techniques in order to estimate the inflation in the average mean squared error due to prior misspecification in the BAC methodology over repeated experiments in a saturated probability model case study. Our findings shed light on future areas for research, and provide users of the BAC methodology with advice on selecting an appropriate prior model for their studies.