  Bayesian models quantify uncertainty and facilitate optimal decision-making
in downstream applications. For most models, however, practitioners are forced
to use approximate inference techniques that lead to sub-optimal decisions due
to incorrect posterior predictive distributions. We present a novel approach
that corrects for inaccuracies in posterior inference by altering the
decision-making process. We train a separate model to make optimal decisions
under the approximate posterior, combining interpretable Bayesian modeling with
optimization of direct predictive accuracy in a principled fashion. The
solution is generally applicable as a plug-in module for predictive
decision-making for arbitrary probabilistic programs, irrespective of the
posterior inference strategy. We demonstrate the approach empirically in
several problems, confirming its potential.
