OBJECTIVE: To review the efficacy of instrumental variable models in addressing a variety of assumption violations to ensure standard ordinary least squares estimates are consistent. Instrumental variable models gained popularity in outcomes research because of their ability to consistently estimate the average causal effects even in the presence of unmeasured confounding. However, in order for this consistent estimation to be achieved several conditions must hold. In this paper, we provide an overview of the instrumental variable approach, examine possible tests to check the prerequisite conditions, and illustrate how weak instruments may produce inconsistent and inefficient results. METHODS: We use two instrumental variables and apply Shea's partial R-square method, the Anderson canonical correlation, and Cragg-Donald tests to check for weak instruments. Hall-Pexie tests are applied to see if any of these instruments are redundant in the analysis. RESULTS: A total of 15,956 asthma patients from a private payer data set were examined in this study. We used controller-reliever copay ratio and physician/practice prescribing patterns as an instrument. We demonstrated that the former was a weak and redundant instrument producing inconsistent and inefficient estimates of the effect of treatment. The results were worse than the results from standard regression analysis. CONCLUSION: Despite the obvious benefit of instrumental variable models, the method should not be used blindly. Several strong conditions are required for these models to work, and each of them should be tested. Otherwise, the results will be statistically worse than the results achieved by simply using standard ordinary least squares.