n overestimate the strength of conclusions[@b26][@b27]. Even with corrections for multiple testing in place, independent tests do not consider the complex multivariable, correlational structure of microbiome data. Furthermore, the final step of inference is based upon a single model that ignores the uncertainty that compounds with each variable selection step prior to building the final model[@b28]. Ensemble multivariable regression modelling approaches such as Bayesian model averaging (BMA) have been used in ecology to address these shortcomings[@b29]. Ensemble methods examine a large number of variable configurations to identify significant variables and to simultaneously compute their effect sizes. After a systematic evaluation of several such ensemble approaches on our mouse microbiome data, we selected BMA for its strong performance across several variable selection and ranking metrics[@b30]. In addition, BMA helped us formally account for model uncertainty in high-dimensions by simultaneously computing two crucial statistical metrics for each finding: a) consistency as measured by posterior inclusion probability (PIP), and b) statistical significance using the 95% CI of effect size[@b31]. Thus, BMA has a distinct advantage over most statistical approaches, which typically provide estimates of statistical significance but not of consistency.

We employed BMA to identify the microbiome community and the host immune response signatures that characterize antibiotic treatment, and to probabilistically rank the influence of the microbiome and the host immune response on the level of *C. albicans* colonization of the mouse GI tract following gastric challenge with this organism. Our analysis revealed that antibiotic-induced changes in the bacterial microbiome were the most consistent determinants of *C. albicans* colonization. These bacterial genera typically influenced the growth of *C. albicans* without simultaneously altering the host GI immune response. Although antibiotics significantly altered the compositio