Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research
这篇综述总结了2012至2017年顶级运营管理期刊中处理内生性的方法，指出常见问题，并通过医疗数据示例演示了工具变量回归的正确使用，对运营管理领域的实证研究者有重要参考价值。
Abstract Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts on empirical results. While premier business journals typically expect authors to rigorously address endogeneity, this expectation is relatively new to many Operations Management (OM) scholars, as exemplified by a recent editorial in Journal of Operations Management that calls for more rigorous treatment for endogeneity. This study serves two purposes. First, we summarize recent OM literature with respect to the treatment for endogeneity by reviewing studies published in leading OM journals between 2012 and 2017. The review provides evidence that endogeneity problems have received increasing attention from OM scholars. However, we also find some common problems that may render the chosen techniques for addressing endogeneity less effective and potentially lead to biased analysis results. Second, since instrumental variable regression is the most prevalent technique for dealing with endogeneity in the OM literature according to our review, we provide an empirical illustration tailored to OM researchers for using instrumental variable regression in the post‐design (data analysis) phase. Using variables from a publicly available healthcare dataset, our analysis sheds light on the importance of examining instruments' quality and triangulating results based on more than one test/estimator.