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137378.2 |
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|a dc
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|a Bertsimas, Dimitris J
|e author
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|a Sloan School of Management
|e contributor
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|a Massachusetts Institute of Technology. Operations Research Center
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|a McCord, Christopher
|e author
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|a Optimization over Continuous and Multi-dimensional Decisions with Observational Data
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|c 2022-01-07T15:48:54Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137378.2
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|a © 2018 Curran Associates Inc.All rights reserved. We consider the optimization of an uncertain objective over continuous and multidimensional 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.
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|a en
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|a Article
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|t Advances in Neural Information Processing Systems
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