Abstract
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.
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Grant support by National Heart, Lung, and Blood Institute R01HL086919, Agency for Healthcare Research and Quality R01HS018403, National Science Foundation SES 0961971.
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Baiocchi, M., Small, D.S., Yang, L. et al. Near/far matching: a study design approach to instrumental variables. Health Serv Outcomes Res Method 12, 237–253 (2012). https://doi.org/10.1007/s10742-012-0091-0
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DOI: https://doi.org/10.1007/s10742-012-0091-0