Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-from-Home Productivity

Sujin Park (), Ali Tafti () and Galit Shmueli ()
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Sujin Park: College of Business Administration, University of Illinois at Chicago, Chicago, Illinois 60607
Ali Tafti: College of Business Administration, University of Illinois at Chicago, Chicago, Illinois 60607
Galit Shmueli: Institute of Service Science, National Tsing Hua University, Hsinchu 30013, Taiwan

Information Systems Research, 2024, vol. 35, issue 2, 686-705

Abstract: Transportability is a structural causal modeling approach aimed at “transporting” a causal effect from a randomized experimental study in one population to a different population where only observational data are available. It offers a way to overcome the practical constraints in inferring causal relationships, such as endogeneity concerns in observational data and the infeasibility of replicating certain experiments. Although transportability holds significant promise for research and practice, it has thus far seldom been implemented in practice, likely because of the lack of practical guidelines for application of transportability theory or the lack of guidance on handling the statistical challenges that might arise. Using a practical problem as an illustration—estimating the effect of telecommuting on worker productivity—we attempt to bridge the theory-practice gap and delineate some challenges faced when putting transportability theory to practice. We offer a detailed procedure for transporting a causal effect across different populations, and we discuss some practical considerations for its implementation, including how to conceptualize causal diagrams, determine the feasibility of transport, select an appropriate diagram, and evaluate its credibility. We also discuss the current limitations, challenges, and opportunities for future research on transportability that would make it more amenable for broad practical use.

Keywords: transportability; causal inference; causal diagram; work-from-home productivity; structural causal modeling; randomized experiment; observational data (search for similar items in EconPapers)
Date: 2024
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