Increasingly, the statistical and epidemiologic literature is focusing beyond issues of internal validity and turning its attention to questions of external validity. Here, we discuss some of the challenges of transporting a causal effect from a randomized trial to a specific target population. We present an inverse odds weighting approach that can easily operationalize transportability. We derive these weights in closed form and illustrate their use with a simple numerical example. We discuss how the conditions required for the identification of internally valid causal effects are translated to apply to the identification of externally valid causal effects. Estimating effects in target populations is an important goal, especially for policy or clinical decisions. Researchers and policy-makers should therefore consider use of statistical techniques such as inverse odds of sampling weights, which under careful assumptions can transport effect estimates from study samples to target populations.

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Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely: conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example.