Near/far matching: A study design approach to instrumental variables

Mike Baiocchi, Dylan S. Small, Lin Yang, Daniel Polsky, Peter W. Groeneveld

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Unlike propensity score matching, though similar to instrumental variable techniques, near/far matching is also capable of estimating causal effects even when unmeasured covariates produce selection bias. We illustrate near/far matching by using Medicare data to compare the effectiveness of carotid arterial stents with cerebral protection versus carotid endarterectomy for the treatment of carotid stenosis.

Original languageEnglish (US)
Pages (from-to)237-253
Number of pages17
JournalHealth Services and Outcomes Research Methodology
Issue number4
StatePublished - Dec 2012
Externally publishedYes


  • Binary outcomes
  • Comparative effectiveness
  • Instrumental variables
  • Matching
  • Medicare data
  • Study design

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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