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On doubly robust estimation in a semiparametric odds ratio model

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  • Eric J. Tchetgen Tchetgen
  • James M. Robins
  • Andrea Rotnitzky

Abstract

We consider the doubly robust estimation of the parameters in a semiparametric conditional odds ratio model. Our estimators are consistent and asymptotically normal in a union model that assumes either of two variation independent baseline functions is correctly modelled but not necessarily both. Furthermore, when either outcome has finite support, our estimators are semiparametric efficient in the union model at the intersection submodel where both nuisance functions models are correct. For general outcomes, we obtain doubly robust estimators that are nearly efficient at the intersection submodel. Our methods are easy to implement as they do not require the use of the alternating conditional expectations algorithm of Chen (2007). Copyright 2010, Oxford University Press.

Suggested Citation

  • Eric J. Tchetgen Tchetgen & James M. Robins & Andrea Rotnitzky, 2010. "On doubly robust estimation in a semiparametric odds ratio model," Biometrika, Biometrika Trust, vol. 97(1), pages 171-180.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:1:p:171-180
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    File URL: http://hdl.handle.net/10.1093/biomet/asp062
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    Citations

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    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    2. Amanda Coston & Edward H. Kennedy, 2022. "The role of the geometric mean in case-control studies," Papers 2207.09016, arXiv.org.
    3. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
    5. Oliver Dukes & Torben Martinussen & Eric J. Tchetgen Tchetgen & Stijn Vansteelandt, 2019. "On doubly robust estimation of the hazard difference," Biometrics, The International Biometric Society, vol. 75(1), pages 100-109, March.
    6. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
    7. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
    8. Tan, Zhiqiang, 2019. "On doubly robust estimation for logistic partially linear models," Statistics & Probability Letters, Elsevier, vol. 155(C), pages 1-1.
    9. Dridi, Ichrak & Boughrara, Adel, 2023. "Flexible inflation targeting and stock market volatility: Evidence from emerging market economies," Economic Modelling, Elsevier, vol. 126(C).
    10. van Amsterdam Wouter A. C. & Ranganath Rajesh, 2023. "Conditional average treatment effect estimation with marginally constrained models," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-26, January.
    11. Nicola Orsini & Rino Bellocco & Arvid Sjolander, 2013. "Doubly robust estimation in generalized linear models," Stata Journal, StataCorp LP, vol. 13(1), pages 185-205, March.

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