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Sensitivity Analysis: Distributional Assumptions and Confounding Assumptions

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  • Tyler J. VanderWeele

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  • Tyler J. VanderWeele, 2008. "Sensitivity Analysis: Distributional Assumptions and Confounding Assumptions," Biometrics, The International Biometric Society, vol. 64(2), pages 645-649, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:645-649
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01024.x
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    References listed on IDEAS

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    1. Tyler J. VanderWeele, 2008. "The Sign of the Bias of Unmeasured Confounding," Biometrics, The International Biometric Society, vol. 64(3), pages 702-706, September.
    2. 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. Tenglong Li & Kenneth A. Frank, 2020. "The probability of a robust inference for internal validity and its applications in regression models," Papers 2005.12784, arXiv.org.
    2. Byeong Yeob Choi & Jason P. Fine & Roman Fernandez & M. Alan Brookhart, 2022. "Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 637-659, September.
    3. Nan Xuan Lin & Stuart Logan & William Edward Henley, 2013. "Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 850-860, December.
    4. Tenglong Li & Ken Frank, 2022. "The probability of a robust inference for internal validity," Sociological Methods & Research, , vol. 51(4), pages 1947-1968, November.
    5. P. Gustafson & L. C. McCandless & A. R. Levy & S. Richardson, 2010. "Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders," Biometrics, The International Biometric Society, vol. 66(4), pages 1129-1137, December.
    6. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    7. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.

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