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Epidemiologists and applied statisticians often believe that relative effectmeasures conditional on covariates, such as risk ratios and mean ratios, are``transportable'' across populations. Here, we examine the identification ofcausal effects in a target population using an assumption that conditionalrelative effect measures (e.g., conditional risk ratios or mean ratios) aretransportable from a trial to the target population. We show thattransportability for relative effect measures is largely incompatible withtransportability for difference effect measures, unless the treatment has noeffect on average or one is willing to make even stronger transportabilityassumptions, which imply the transportability of both relative and differenceeffect measures. We then describe how marginal causal estimands in a targetpopulation can be identified under the assumption of transportability ofrelative effect measures, when we are interested in the effectiveness of a newexperimental treatment in a target population where the only treatment in useis the control treatment evaluated in the trial. We extend these results toconsider cases where the control treatment evaluated in the trial is only oneof the treatments in use in the target population, under an additional partialexchangeability assumption in the target population (i.e., a partial assumptionof no unmeasured confounding in the target population). We also developidentification results that allow for the covariates needed fortransportability of relative effect measures to be only a small subset of thecovariates needed to control confounding in the target population. Last, wepropose estimators that can be easily implemented in standard statisticalsoftware.

Learning about treatment effects in a new target population under transportability assumptions for relative effect measures