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Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data

Author

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  • Alexander G. Nikolaev

    (Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, New York 14260)

  • Sheldon H. Jacobson

    (Department of Computer Science, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801)

  • Wendy K. Tam Cho

    (Departments of Political Science and Statistics and the National Center for Supercomputing Applications, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801)

  • Jason J. Sauppe

    (Department of Computer Science, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801)

  • Edward C. Sewell

    (Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62026)

Abstract

Scientists in all disciplines attempt to identify and document causal relationships. Those not fortunate enough to be able to design and implement randomized control trials must resort to observational studies. To make causal inferences outside the experimental realm, researchers attempt to control for bias sources by postprocessing observational data. Finding the subset of data most conducive to unbiased or least biased treatment effect estimation is a challenging, complex problem. However, the rise in computational power and algorithmic sophistication leads to an operations research solution that circumvents many of the challenges presented by methods employed over the past 30 years.

Suggested Citation

  • Alexander G. Nikolaev & Sheldon H. Jacobson & Wendy K. Tam Cho & Jason J. Sauppe & Edward C. Sewell, 2013. "Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data," Operations Research, INFORMS, vol. 61(2), pages 398-412, April.
    • Handle: RePEc:inm:oropre:v:61:y:2013:i:2:p:398-412
    DOI: 10.1287/opre.1120.1118
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    2. Shouvik Dutta & Jason Sauppe & Sheldon Jacobson, 2016. "Targeted Marketing Using Balance Optimization Subset Selection," Annals of Data Science, Springer, vol. 3(4), pages 423-444, December.
    3. Md Saiful Islam & Md Sarowar Morshed & Md. Noor-E-Alam, 2022. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3023-3041, November.
    4. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
    5. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    6. Dutta Shouvik & Jacobson Sheldon H. & Sauppe Jason J., 2017. "Identifying NCAA tournament upsets using Balance Optimization Subset Selection," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(2), pages 79-93, June.
    7. Md Saiful Islam & Md Sarowar Morshed & Gary J Young & Md Noor-E-Alam, 2019. "Robust policy evaluation from large-scale observational studies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    8. Hochbaum, Dorit S. & Rao, Xu & Sauppe, Jason, 2022. "Network flow methods for the minimum covariate imbalance problem," European Journal of Operational Research, Elsevier, vol. 300(3), pages 827-836.
    9. Hee Youn Kwon & Jason J. Sauppe & Sheldon H. Jacobson, 2019. "Treatment Effect Decomposition and Bootstrap Hypothesis Testing in Observational Studies," Annals of Data Science, Springer, vol. 6(3), pages 491-511, September.
    10. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.

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