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Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas · Christopher McCord

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 210 #39

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.

Author Information

Dimitris Bertsimas (Massachusetts Institute of Technology)
Christopher McCord (MIT)

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