OM Forum—Causal Inference Models in Operations Management

Teck Ho (), Noah Lim (), Sadat Reza () and Xiaoyu Xia ()
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Noah Lim: National University of Singapore, Singapore 119077; and University of Wisconsin–Madison, Madison, Wisconsin 53706
Sadat Reza: Institute on Asian Consumer Insight, Nanyang Technological University, Singapore 639798
Xiaoyu Xia: Department of Decision Sciences and Managerial Economics, CUHK Business School, Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Manufacturing & Service Operations Management, 2017, vol. 19, issue 4, 509-525

Abstract: 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.

Keywords: empirical research; econometric analysis; causal inference; big data (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (13)

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