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Dissonant Conclusions When Testing the Validity of an Instrumental Variable

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  • Fan Yang
  • José R. Zubizarreta
  • Dylan S. Small
  • Scott Lorch
  • Paul R. Rosenbaum

Abstract

An instrument or instrumental variable is often used in an effort to avoid selection bias in inference about the effects of treatments when treatment choice is based on thoughtful deliberation. Instruments are increasingly used in health outcomes research. An instrument is a haphazard push to accept one treatment or another, where the push can affect outcomes only to the extent that it alters the treatment received. There are two key assumptions here: (R) the push is haphazard or essentially random once adjustments have been made for observed covariates, (E) the push affects outcomes only by altering the treatment, the so-called "exclusion restriction." These assumptions are often said to be untestable; however, that is untrue if testable means checking the compatibility of assumptions with other things we think we know. A test of this sort may result in a collection of claims that are individually plausible but mutually inconsistent, without clear indication as to which claim is culpable for the inconsistency. We discuss this subject in the context of our on-going study of the effects of delivery by cesarean section on the survival of extremely premature infants of 23-24 weeks gestational age. Supplementary materials for this article are available online.

Suggested Citation

  • Fan Yang & José R. Zubizarreta & Dylan S. Small & Scott Lorch & Paul R. Rosenbaum, 2014. "Dissonant Conclusions When Testing the Validity of an Instrumental Variable," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 253-263, November.
    • Handle: RePEc:taf:amstat:v:68:y:2014:i:4:p:253-263
    DOI: 10.1080/00031305.2014.962764
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    References listed on IDEAS

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    1. Rosenbaum, Paul R. & Silber, Jeffrey H., 2009. "Amplification of Sensitivity Analysis in Matched Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1398-1405.
    2. Brookhart M. Alan & Schneeweiss Sebastian, 2007. "Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-25, December.
    3. Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
    4. Guido W. Imbens & Paul R. Rosenbaum, 2005. "Robust, accurate confidence intervals with a weak instrument: quarter of birth and education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 109-126, January.
    5. Jing Cheng & Jing Qin & Biao Zhang, 2009. "Semiparametric estimation and inference for distributional and general treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 881-904, September.
    6. Dubay, Lisa & Kaestner, Robert & Waidmann, Timothy, 1999. "The impact of malpractice fears on cesarean section rates," Journal of Health Economics, Elsevier, vol. 18(4), pages 491-522, August.
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    Cited by:

    1. Luke Keele & Dylan Small & Richard Grieve, 2017. "Randomization-based instrumental variables methods for binary outcomes with an application to the ‘IMPROVE’ trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 569-586, February.
    2. Koppenberg, Maximilian & Mishra, Ashok K. & Hirsch, Stefan, 2023. "Food aid and violent conflict: A review and Empiricist’s companion," Food Policy, Elsevier, vol. 121(C).
    3. Wenqi Hu & Carri W. Chan & José R. Zubizarreta & Gabriel J. Escobar, 2018. "An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 531-549, July.
    4. Colin B. Fogarty & Pixu Shi & Mark E. Mikkelsen & Dylan S. Small, 2017. "Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 321-331, January.
    5. Luke Keele & Steve Harris & Samuel D. Pimentel & Richard Grieve, 2020. "Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1501-1521, October.
    6. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    7. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
    8. Koppenberg, Maximilian & Mishra, Ashok K. & Hirsch, Stefan, 2023. "Food Aid and Violent Conflict: A Review of Literature," IZA Discussion Papers 16574, Institute of Labor Economics (IZA).
    9. Hyunseung Kang & Laura Peck & Luke Keele, 2018. "Inference for instrumental variables: a randomization inference approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1231-1254, October.
    10. Jiayi Liu & Jingui Xie & Kum Khiong Yang & Zhichao Zheng, 2019. "Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 780-797, October.
    11. Zou, Baoling & Chen, Yudan & Mishra, Ashok K. & Hirsch, Stefan, 2024. "Agricultural mechanization and the performance of the local Chinese economy," Food Policy, Elsevier, vol. 125(C).

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