Duality in balance optimization subset selection

Hee Youn Kwon (), Jason J. Sauppe () and Sheldon H. Jacobson
Additional contact information
Hee Youn Kwon: Northwestern University
Jason J. Sauppe: University of Wisconsin-La Crosse
Sheldon H. Jacobson: University of Illinois at Urbana-Champaign

Annals of Operations Research, 2020, vol. 289, issue 2, No 8, 277-289

Abstract: Abstract In this paper, we investigate a specific optimization problem that arises in the context of Balance Optimization Subset Selection (BOSS), which is an optimization framework for causal inference. Most BOSS problems can be formulated as mixed integer linear programs. By relaxing the integrality constraints so that fractional contributions of control units are permitted, a linear program (LP) is obtained. Properties of this LP and its dual are investigated and a sensitivity analysis is conducted to characterize how the objective value changes as the covariate values are perturbed.

Keywords: Linear programming; Duality; Optimization for causal analysis (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-020-03513-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-020-03513-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-020-03513-y

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().