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Optimal Nonbipartite Matching and Its Statistical Applications

Author

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  • Lu, Bo
  • Greevy, Robert
  • Xu, Xinyi
  • Beck, Cole

Abstract

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  • Lu, Bo & Greevy, Robert & Xu, Xinyi & Beck, Cole, 2011. "Optimal Nonbipartite Matching and Its Statistical Applications," The American Statistician, American Statistical Association, vol. 65(1), pages 21-30.
    • Handle: RePEc:bes:amstat:v:65:i:1:y:2011:p:21-30
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    Citations

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    Cited by:

    1. Dan Yang & Dylan S. Small & Jeffrey H. Silber & Paul R. Rosenbaum, 2012. "Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes," Biometrics, The International Biometric Society, vol. 68(2), pages 628-636, June.
    2. Jinglong Zhao, 2023. "Adaptive Neyman Allocation," Papers 2309.08808, arXiv.org, revised Sep 2023.
    3. Douglas Lehmann & Yun Li & Rajiv Saran & Yi Li, 2017. "Strengthening Instrumental Variables Through Weighting," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 320-338, December.
    4. Bikram Karmakar, 2022. "An approximation algorithm for blocking of an experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1726-1750, November.
    5. Biswas, Munmun & Ghosh, Anil K., 2014. "A nonparametric two-sample test applicable to high dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 160-171.
    6. Samuel D. Pimentel & Dylan S. Small & Paul R. Rosenbaum, 2016. "Constructed Second Control Groups and Attenuation of Unmeasured Biases," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1157-1167, July.
    7. Bo Lu & Zhenchao Qian & Anna Cunningham & Chih-Lin Li, 2012. "Estimating the Effect of Premarital Cohabitation on Timing of Marital Disruption," Sociological Methods & Research, , vol. 41(3), pages 440-466, August.
    8. Chowdbury, Shyamal & Schildberg-Hörisch, Hannah & Schneider, Sebastian O. & Sutter, Matthias, 2022. "Information provision over the phone saves lives: An RCT to contain COVID-19 in rural Bangladesh at the pandemic's onset," DICE Discussion Papers 393, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    9. Jinglong Zhao & Zijie Zhou, 2022. "Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective," Papers 2201.12936, arXiv.org, revised May 2024.
    10. Petrie, Adam, 2016. "Graph-theoretic multisample tests of equality in distribution for high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 145-158.
    11. 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.
    12. Rüdiger Mutz & Hans-Dieter Daniel, 2012. "The generalized propensity score methodology for estimating unbiased journal impact factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 377-390, August.
    13. Lamar Pierce & Alex Rees-Jones & Charlotte Blank, 2020. "The Negative Consequences of Loss-Framed Performance Incentives," NBER Working Papers 26619, National Bureau of Economic Research, Inc.
    14. Paul R. Rosenbaum, 2013. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies," Biometrics, The International Biometric Society, vol. 69(1), pages 118-127, March.
    15. Zhang, Yuyang & Schnell, Patrick & Song, Chi & Huang, Bin & Lu, Bo, 2021. "Subgroup causal effect identification and estimation via matching tree," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    16. Bo Zhang & Siyu Heng & Emily J. MacKay & Ting Ye, 2022. "Bridging preference‐based instrumental variable studies and cluster‐randomized encouragement experiments: Study design, noncompliance, and average cluster effect ratio," Biometrics, The International Biometric Society, vol. 78(4), pages 1639-1650, December.
    17. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.
    18. Kikuta,Kyosuke, 2022. "The drowning-out effect: voter turnout, uncertainty, and protests," IDE Discussion Papers 867, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    19. Jiayi Liu & Jingui Xie & Kum Khiong Yang & Zhichao Zheng, 2019. "Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 780-797, October.
    20. Aufenanger, Tobias, 2018. "Treatment allocation for linear models," FAU Discussion Papers in Economics 14/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2018.
    21. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
    22. José R. Zubizarreta, 2012. "Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1360-1371, December.

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