Optimization over Continuous and Multi-dimensional Decisions with Observational Data
This addresses optimization challenges in data-driven decision-making for domains like operations research or economics, though it appears incremental as it builds on existing predictive methods.
The authors tackled the problem of optimizing uncertain objectives over continuous, multi-dimensional decision spaces using only observational data, proposing a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods in example problems.
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.