OM Forum—Causal Inference Models in Operations Management
介绍了五种经济学中广泛使用的因果推断工具，帮助运营管理研究者应对内生性和选择偏差挑战，并通过大数据属性（多样性、速度、体量）的示例说明如何缓解内生性偏差。
Operations management (OM) researchers have traditionally focused on developing normative mathematical models that prescribe what managers and firms should do. Recently, there has been increased interest in understanding what managers and firms actually do and the factors that drive these decisions. To advance this understanding, empirical investigation using causal inference models is critical. However, in many contexts, the ability to obtain causal inferences is fraught with the challenges of endogeneity and selection bias. This paper describes five empirical tools that have been widely used in economics to address these challenges and how they can be adopted by OM researchers. We also present an example that illustrates how the various attributes of big data—variety, velocity, and volume—can be useful in addressing the endogeneity bias. The online appendix is available at https://doi.org/10.1287/msom.2017.0659 .