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An optimization approach for making causal inferences

Author

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  • Wendy K. Tam Cho
  • Jason J. Sauppe
  • Alexander G. Nikolaev
  • Sheldon H. Jacobson
  • Edward C. Sewell

Abstract

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Suggested Citation

  • Wendy K. Tam Cho & Jason J. Sauppe & Alexander G. Nikolaev & Sheldon H. Jacobson & Edward C. Sewell, 2013. "An optimization approach for making causal inferences," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(2), pages 211-226, May.
    • Handle: RePEc:bla:stanee:v:67:y:2013:i:2:p:211-226
    DOI: 10.1111/(ISSN)1467-9574
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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. 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.
    3. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
    4. 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.
    5. 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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