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Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls

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  • Kewei Ming
  • Paul R. Rosenbaum

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  • Kewei Ming & Paul R. Rosenbaum, 2000. "Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls," Biometrics, The International Biometric Society, vol. 56(1), pages 118-124, March.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:1:p:118-124
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    File URL: https://hdl.handle.net/10.1111/j.0006-341X.2000.00118.x
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    Cited by:

    1. Sameer K Deshpande & Raiden B Hasegawa & Jordan Weiss & Dylan S Small, 2020. "The association between adolescent football participation and early adulthood depression," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-14, March.
    2. Bo Lu, 2005. "Propensity Score Matching with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 721-728, September.
    3. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
    4. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    5. repec:zbw:rwidps:0021 is not listed on IDEAS
    6. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    7. Howard Birnbaum & Crystal Pike & Ritesh Banerjee & Tracy Waldman & Mary Cifaldi, 2012. "Changes in Utilization and Costs for Patients with Rheumatoid Arthritis, 1997 to 2006," PharmacoEconomics, Springer, vol. 30(4), pages 323-336, April.
    8. Colin B. Fogarty & Pixu Shi & Mark E. Mikkelsen & Dylan S. Small, 2017. "Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 321-331, January.
    9. Michela Bia & Roberto Leombruni & Pierre-Jean Messe, 2009. "Young in-Old out: a new evaluation based on Generalized Propensity Score," LABORatorio R. Revelli Working Papers Series 93, LABORatorio R. Revelli, Centre for Employment Studies.
    10. Zhenzhen Xu & John D. Kalbfleisch, 2013. "Repeated Randomization and Matching in Multi-Arm Trials," Biometrics, The International Biometric Society, vol. 69(4), pages 949-959, December.
    11. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    12. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    13. 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.
    14. Phuong Nguyen-Hoang, 2012. "Fiscal effects of budget referendums: evidence from New York school districts," Public Choice, Springer, vol. 150(1), pages 77-95, January.
    15. Sylvia Brandt & Sara Gale & Ira Tager, 2012. "The value of health interventions: evaluating asthma case management using matching," Applied Economics, Taylor & Francis Journals, vol. 44(17), pages 2245-2263, June.
    16. Augurzky, Boris & Kluve, Jochen, 2004. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 21, RWI - Leibniz-Institut für Wirtschaftsforschung.

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