When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This article presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, and the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

中文翻译：

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普遍性和可移植性回顾


在评估因果效应时，确定结果要推广到的目标人群是一个关键的决定。随机研究和观察性研究在估计目标人群的因果效应方面各有优点和局限性。随机数据的估计可能具有内部有效性，但通常不能代表目标人群。观察数据可能更好地反映目标人群，因此更有可能具有外部有效性，但由于未测量的混杂因素而可能存在潜在偏差。虽然许多因果推理文献都集中于解决内部有效性偏差，但内部有效性和外部有效性对于目标人群的无偏差估计都是必要的。本文提出了一个解决外部有效性偏差的框架，包括概括性和可移植性方法及其所需的假设，以及对治疗效果的异质性以及研究和目标人群之间差异的测试。