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Duality in balance optimization subset selection

delete2020-03-10
delete1
PRE
AI
H
Hee Youn Kwon
J
Jason J. Sauppe *
S
Sheldon H. Jacobson
DOI：10.1007/s10479-020-03513-ydelete
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Abstract

Abstract

En 中文
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
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Journal

Annals of Operations Research cover
Annals of Operations Research
IF:
4.5
Papers:
8.0K
Citations:
2.1W

Organization

U
University of Illinois Urbana-Champaign
Scholars:
2.4W
Papers: 2.0W
Citations: 35
University of Illinois System cover
University of Illinois System
Scholars:
6.8W
Papers: 6.2W
Citations: 644
N
Northwestern University
Scholars:
6.1W
Papers: 5.3W
Citations: 3.9K
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