Data-Driven Sample Average Approximation with Covariate Information

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dc.contributor.authorKannan, Rohiten
dc.contributor.authorBayraksan, Guezinen
dc.contributor.authorLuedtke, James R.en
dc.date.accessioned2025-02-18T13:21:07Zen
dc.date.available2025-02-18T13:21:07Zen
dc.date.issued2025-01-06en
dc.description.abstractWe study optimization for data-driven decision-making when we have observations of the uncertain parameters within an optimization model together with concurrent observations of covariates. The goal is to choose a decision that minimizes the expected cost conditioned on a new covariate observation. We investigate two data-driven frameworks that integrate a machine learning prediction model within a stochastic programming sample average approximation (SAA) for approximating the solution to this problem. One SAA framework is new and uses leave-one-out residuals for scenario generation. The frameworks we investigate are flexible and accommodate parametric, nonparametric, and semiparametric regression techniques. We derive conditions on the data generation process, the prediction model, and the stochastic program under which solutions of these data-driven SAAs are consistent and asymptotically optimal, and also derive finite sample guarantees. Computational experiments validate our theoretical results, demonstrate examples where our datadriven formulations have advantages over existing approaches (even if the prediction model is misspecified), and illustrate the benefits of our data-driven formulations in the limited data regime.en
dc.description.versionAccepted versionen
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1287/opre.2020.0533en
dc.identifier.eissn1526-5463en
dc.identifier.issn0030-364Xen
dc.identifier.orcidKannan, Rohit [0000-0002-7963-7682]en
dc.identifier.urihttps://hdl.handle.net/10919/124636en
dc.language.isoenen
dc.publisherINFORMSen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectdata-driven stochastic programmingen
dc.subjectcovariatesen
dc.subjectregressionen
dc.subjectsample average approximationen
dc.subjectjackknifeen
dc.subjectlarge deviationsen
dc.titleData-Driven Sample Average Approximation with Covariate Informationen
dc.title.serialOperations Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

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