Instrumental Variable Analysis

  • Michael BaiocchiEmail author
  • Jing Cheng
  • Dylan S. Small
Reference work entry
Part of the Health Services Research book series (HEALTHSR)


A goal of many health services research studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead an observational study must be used. A major difficulty with observational studies is that there might be unmeasured confounding, i.e., unmeasured ways in which the treatment and control groups differ before treatment that affect the outcome. Instrumental variable analysis is a method for controlling for unmeasured confounding. Instrumental variable analysis requires the measurement of a valid instrumental variable, which is a variable that is independent of the unmeasured confounding and encourages a subject to take one treatment level versus another, while having no effect on the outcome beyond its encouragement of a certain treatment level. This chapter discusses the types of causal effects that can be estimated by instrumental variable analysis, the assumptions needed for instrumental variable analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions, methods of estimation of causal effects using instrumental variables, and sources of instrumental variables in health services research studies.



Jing Cheng and Dylan Small were supported by grant RC4MH092722 from the National Institute of Mental Health. The authors thank Scott Lorch for the use of the data from the NICU study.


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Authors and Affiliations

  1. 1.Department of StatisticsStanford UniversityStanfordUSA
  2. 2.Department of Preventive and Restorative Dental SciencesUniversity of California, San Francisco School of DentistrySan FranciscoUSA
  3. 3.University of PennsylvaniaPhiladelphiaUSA

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