Baiocchi, MikeSmall, Dylan SYang, LinPolsky, DanielGroeneveld, Peter W2023-05-232023-05-232012-12-012016-07-11https://repository.upenn.edu/handle/20.500.14332/47946Classic 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.The final publication is available at Springer via http://dx.doi.org/10.1007/s10742-012-0091-0.instrumental variablesmatchingstudy designbinary outcomescomparative effectivenessmedicare dataOther Statistics and ProbabilityVital and Health StatisticsArticle