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A review of instrumental variables estimation in the applied health sciences

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  • Paul Grootendorst

Abstract

Health scientists often use observational data to estimate treatment effects when controlled experiments are not feasible. A limitation of observational research is non-random selection of subjects into different treatments, potentially leading to selection bias. The 2 commonly used solutions to this problem – covariate adjustment and fully parametric models – are limited by strong and untestable assumptions. Instrumental variables estimation can be a viable alternative. In this paper, I review examples of the application of IV in the health and social sciences, I show how the IV estimator works, I discuss the factors that affect its performance, I review how the interpretation of the IV estimator changes when treatment effects vary by individual, and consider the application of IV to nonlinear models.

Suggested Citation

  • Paul Grootendorst, 2007. "A review of instrumental variables estimation in the applied health sciences," Social and Economic Dimensions of an Aging Population Research Papers 215, McMaster University.
    • Handle: RePEc:mcm:sedapp:215
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    File URL: http://socserv.mcmaster.ca/sedap/p/sedap215.pdf
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    References listed on IDEAS

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    1. Andrew M. Jones (ed.), 2006. "The Elgar Companion to Health Economics," Books, Edward Elgar Publishing, number 3572.
    2. Jinyong Hahn & Jerry Hausman, 2002. "A New Specification Test for the Validity of Instrumental Variables," Econometrica, Econometric Society, vol. 70(1), pages 163-189, January.
    3. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, April.
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    1. Daniel Solís & Boris E. Bravo‐Ureta & Ricardo E. Quiroga, 2009. "Technical Efficiency among Peasant Farmers Participating in Natural Resource Management Programmes in Central America," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 202-219, February.
    2. René Algesheimer & Sharad Borle & Utpal M. Dholakia & Siddharth S. Singh, 2010. "The Impact of Customer Community Participation on Customer Behaviors: An Empirical Investigation," Marketing Science, INFORMS, vol. 29(4), pages 756-769, 07-08.
    3. Jasjeet Singh Sekhon & Richard D. Grieve, 2012. "A matching method for improving covariate balance in cost‐effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 695-714, June.
    4. Nshakira-Rukundo, Emmanuel & Mussa, Essa Chanie & Gerber, Nicolas & von Braun, Joachim, 2020. "Impact of voluntary community-based health insurance on child stunting: Evidence from rural Uganda," Social Science & Medicine, Elsevier, vol. 245(C).
    5. Gabriela V. Cohen Freue & Hernan Ortiz-Molina & Ruben H. Zamar, 2013. "A Natural Robustification of the Ordinary Instrumental Variables Estimator," Biometrics, The International Biometric Society, vol. 69(3), pages 641-650, September.
    6. Denny, Kevin J., 2011. "Instrumental variable estimation of the effect of prayer on depression," Social Science & Medicine, Elsevier, vol. 73(8), pages 1194-1199.

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    More about this item

    Keywords

    instrumental variables; treatment effects; health outcomes;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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