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Duality in balance optimization subset selection

Author

Listed:
  • Hee Youn Kwon

    (Northwestern University
    Northwestern Institute on Complex Systems (NICO))

  • Jason J. Sauppe

    (University of Wisconsin-La Crosse)

  • Sheldon H. Jacobson

    (University of Illinois at Urbana-Champaign)

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.

Suggested Citation

  • Hee Youn Kwon & Jason J. Sauppe & Sheldon H. Jacobson, 2020. "Duality in balance optimization subset selection," Annals of Operations Research, Springer, vol. 289(2), pages 277-289, June.
  • Handle: RePEc:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-020-03513-y
    DOI: 10.1007/s10479-020-03513-y
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