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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.

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File URL: http://socserv.mcmaster.ca/sedap/p/sedap215.pdf
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Bibliographic Info

Paper provided by McMaster University in its series Social and Economic Dimensions of an Aging Population Research Papers with number 215.

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Length: 32 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:mcm:sedapp:215

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Keywords: instrumental variables; treatment effects; health outcomes;

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  1. 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.
  2. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
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