  We present methods for causally interpretable meta-analyses that combine
information from multiple randomized trials to estimate potential
(counterfactual) outcome means and average treatment effects in a target
population. We consider identifiability conditions, derive implications of the
conditions for the law of the observed data, and obtain identification results
for transporting causal inferences from a collection of independent randomized
trials to a new target population in which experimental data may not be
available. We propose an estimator for the potential (counterfactual) outcome
mean in the target population under each treatment studied in the trials. The
estimator uses covariate, treatment, and outcome data from the collection of
trials, but only covariate data from the target population sample. We show that
it is doubly robust, in the sense that it is consistent and asymptotically
normal when at least one of the models it relies on is correctly specified. We
study the finite sample properties of the estimator in simulation studies and
demonstrate its implementation using data from a multi-center randomized trial.
