The purpose of the article “Identification of Causal Effects Using Instrumental Variables,” is to define an outline for causal inference in situations under which allocation to a binary treatment can be ignored but the compliance with such an assignment is critical. In their discussion, Angrist, Guido, and Donald describe the statistical technique that can be used to reduce possible errors, which arise in a randomized assignment without perfect compliance (445). Accordingly, the article explains the Instrumental Variables (IV) approach, which suits the Ruben Causal Model. Thus, the Angrist and his colleges illustrate the significance of IV in reducing errors when comparing different subjects in randomized trials.

According to the authors, IV provides a more explicit interpretation of the crucial assumptions involved in causal analyses. Additionally, the technique improves sensitivity by helping to identify the deviation from the premises. Moreover, based on the journal, economists employ several statistical methodologies, such as Structural Equation Models to evaluate the casual effects of random treatments (445).Such methods were aimed at establishing the relationship among variables. Nevertheless, Angrist, Guido, and Donald demonstrate the merits of using IV alongside Ruben Causal Model in economic analysis.

In conclusion, “Identification of Causal Effects Using Instrumental Variables,” assumes that standard IV is a reliable method for computing economic data. The technique works effectively with Ruben Causal Model to simplify the process of identifying critical assumptions and the sensitivity of possible deviations from the premises. Moreover, using standard IV, it was easy to reduce errors in random assignments.

Work Cited

Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association, vol. 91, no.434, 1996, p.444-472.

 
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