We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowledge of population heterogeneity or summary-level statistics to achieve improved efficiency. We first propose a one-step semiparametric estimator that assumes knowledge of the baseline covariates driving the heterogeneity across populations. This approach improves efficiency by transporting only through these covariates, rather than through the full covariate set. We then introduce a collaborative one-step semiparametric estimator that also improves efficiency without requiring knowledge of which covariates drive study heterogeneity. Collectively, these estimators advance our ability to transport causal effects by improving efficiency in the presence of mediation and intermediate confounding through targeted dimension reduction strategies. We use simulation to examine finite-sample performance and apply our estimators to a large-scale housing mobility trial (MTO) and a hearing intervention trial (ACHIEVE).

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