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Double-Robust Estimators: Slightly More Bayesian than Meets the Eye?

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  • Gustafson Paul

    (University of British Columbia)

Abstract

Consider the simple setting of point exposure, outcome and confounding variables, all of which are discrete. As is well known, parametric modeling of outcome given exposure and confounders and also exposure given confounders can yield a double-robust estimator. This has the property of being consistent as long as at least one of the two specified models is correct. Such an estimator can also be cast as arising from a compromise between the parametric outcome model and a nonparametric or saturated outcome model. This brings to mind an alternate compromise based on Bayesian model averaging, and prompts comparisons between the double-robust method and the Bayesian method.

Suggested Citation

  • Gustafson Paul, 2012. "Double-Robust Estimators: Slightly More Bayesian than Meets the Eye?," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-15, January.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:2:n:4
    DOI: 10.2202/1557-4679.1349
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    References listed on IDEAS

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    1. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, September.
    2. Lefebvre Geneviève & Gustafson Paul, 2010. "Impact of Outcome Model Misspecification on Regression and Doubly-Robust Inverse Probability Weighting to Estimate Causal Effect," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-27, March.
    3. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    4. 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.
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    Cited by:

    1. O. Saarela & L. R. Belzile & D. A. Stephens, 2016. "A Bayesian view of doubly robust causal inference," Biometrika, Biometrika Trust, vol. 103(3), pages 667-681.
    2. Luo, Yu & Graham, Daniel J. & McCoy, Emma J., 2023. "Semiparametric Bayesian doubly robust causal estimation," LSE Research Online Documents on Economics 117944, London School of Economics and Political Science, LSE Library.
    3. Alessandra Mattei & Fabrizia Mealli, 2015. "Discussion of “On Bayesian Estimation of Marginal Structural Models”," Biometrics, The International Biometric Society, vol. 71(2), pages 293-296, June.

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