Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-from-Home Productivity
以居家办公对生产力的影响为例，详细说明了如何将随机实验的因果效应迁移到只有观测数据的目标群体，并讨论了实际应用中的步骤、挑战和未来方向。
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 allows for extracting much more value from randomized control trials because under some conditions, it allows the estimation of causal effects in a target population where replicating the experiment is difficult, costly, or impossible. Despite the enormous economic and social benefits of transportability, it has thus far seldom been implemented in practice, likely because of the lack guidelines for applying transportability theory in practice and 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 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.