A Review of Generalizability and Transportability

Abstract

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 paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

30 pages, 3 figures

References in corpus (4)

• Matching Methods for Causal Inference: A Review and a Look Forward
• External Validity: From Do-Calculus to Transportability Across Populations
• Causal Inference for Comprehensive Cohort Studies
• Generalizing trial findings using nested trial designs with sub-sampling of non-randomized individuals

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