  We consider the optimization of an uncertain objective over continuous and
multi-dimensional decision spaces in problems in which we are only provided
with observational data. We propose a novel algorithmic framework that is
tractable, asymptotically consistent, and superior to comparable methods on
example problems. Our approach leverages predictive machine learning methods
and incorporates information on the uncertainty of the predicted outcomes for
the purpose of prescribing decisions. We demonstrate the efficacy of our method
on examples involving both synthetic and real data sets.
