A review of instrumental variables estimation in the applied health sciences
AbstractHealth 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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Bibliographic InfoPaper provided by McMaster University in its series Social and Economic Dimensions of an Aging Population Research Papers with number 215.
Length: 32 pages
Date of creation: Jun 2007
Date of revision:
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instrumental variables; treatment effects; health outcomes;
Find related papers by 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 Production
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- Jinyong Hahn & Jerry Hausman, 2002.
"A New Specification Test for the Validity of Instrumental Variables,"
Econometric Society, vol. 70(1), pages 163-189, January.
- Jinyong Hahn & Jerry Hausman, 1999. "A New Specification Test for the Validity of Instrumental Variables," Working papers 99-11, Massachusetts Institute of Technology (MIT), Department of Economics.
- Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, June.
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