A review of instrumental variables estimation in the applied health sciences
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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- 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, September. Full references (including those not matched with items on IDEAS)
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