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Double robustness without weighting

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  • Lee, Myoung-jae
  • Lee, Sanghyeok

Abstract

In contrast to the well-known propensity score (PS), the lesser known ‘prognostic score (PGS)’ balances the potential untreated response. This paper shows that ‘double robustness’ can be achieved by controlling both PS and PGS in various ways in a “method-blind” manner.

Suggested Citation

  • Lee, Myoung-jae & Lee, Sanghyeok, 2019. "Double robustness without weighting," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 175-180.
    • Handle: RePEc:eee:stapro:v:146:y:2019:i:c:p:175-180
    DOI: 10.1016/j.spl.2018.11.017
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    References listed on IDEAS

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    1. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    2. Zonghui Hu & Dean A. Follmann & Jing Qin, 2012. "Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 247-257, March.
    3. Zonghui Hu & Dean A. Follmann & Naisyin Wang, 2014. "Estimation of mean response via the effective balancing score," Biometrika, Biometrika Trust, vol. 101(3), pages 613-624.
    4. Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
    5. Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
    6. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    7. Lee, Myoung-jae, 2012. "Treatment effects in sample selection models and their nonparametric estimation," Journal of Econometrics, Elsevier, vol. 167(2), pages 317-329.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    9. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699.
    10. Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.
    11. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    12. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 533-575.
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    Cited by:

    1. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.

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