Causal Inference Models in Operations Management

• 成果类型: Article
• 署名作者: Ho, Teck-Hua; Lim, Noah; Reza, Sadat; Xia, Xiaoyu
• 署名单位: National University of Singapore; University of Wisconsin System; University of Wisconsin Madison; Nanyang Technological University; Chinese University of Hong Kong
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2017.0659
• 发表日期: 2017
• 页码: 509-525
• 关键词: empirical research; econometric analysis; Causal Inference; big data

摘要

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.