  Statistical models often include thousands of parameters. However, large
models decrease the investigator's ability to interpret and communicate the
estimated parameters. Reducing the dimensionality of the parameter space in the
estimation phase is a commonly used approach, but less work has focused on
selecting subsets of the parameters for interpreting the estimated model --
especially in settings such as Bayesian inference and model averaging.
Importantly, many models do not have straightforward interpretations and create
another layer of obfuscation. To solve this gap, we introduce a new method that
uses the Wasserstein distance to identify a low-dimensional interpretable model
projection. After the estimation of complex models, users can budget how many
parameters they wish to interpret and the proposed generates a simplified model
of the desired dimension minimizing the distance to the full model. We provide
simulation results to illustrate the method and apply it to cancer datasets.
