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The Sign of the Bias of Unmeasured Confounding

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  • Tyler J. VanderWeele

Abstract

Summary Unmeasured confounding variables are a common problem in drawing causal inferences in observational studies. A theorem is given which in certain circumstances allows the researcher to draw conclusions about the sign of the bias of unmeasured confounding. Specifically, it is possible to determine the sign of the bias when monotonicity relationships hold between the unmeasured confounding variable and the treatment, and between the unmeasured confounding variable and the outcome. Some discussion is given to the conditions under which the theorem applies and the strengths and limitations of using the theorem to assess the sign of the bias of unmeasured confounding.

Suggested Citation

  • Tyler J. VanderWeele, 2008. "The Sign of the Bias of Unmeasured Confounding," Biometrics, The International Biometric Society, vol. 64(3), pages 702-706, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:702-706
    DOI: 10.1111/j.1541-0420.2007.00957.x
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    References listed on IDEAS

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    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
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    Cited by:

    1. Jiang Zhichao & Chiba Yasutaka & VanderWeele Tyler J., 2014. "Monotone Confounding, Monotone Treatment Selection and Monotone Treatment Response," Journal of Causal Inference, De Gruyter, vol. 2(1), pages 1-12, March.
    2. Tyler J. VanderWeele, 2008. "Sensitivity Analysis: Distributional Assumptions and Confounding Assumptions," Biometrics, The International Biometric Society, vol. 64(2), pages 645-649, June.

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