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We present methods for causally interpretable meta-analyses that combineinformation from multiple randomized trials to estimate potential(counterfactual) outcome means and average treatment effects in a targetpopulation. We consider identifiability conditions, derive implications of theconditions for the law of the observed data, and obtain identification resultsfor transporting causal inferences from a collection of independent randomizedtrials to a new target population in which experimental data may not beavailable. We propose an estimator for the potential (counterfactual) outcomemean in the target population under each treatment studied in the trials. Theestimator uses covariate, treatment, and outcome data from the collection oftrials, but only covariate data from the target population sample. We show thatit is doubly robust, in the sense that it is consistent and asymptoticallynormal when at least one of the models it relies on is correctly specified. Westudy the finite sample properties of the estimator in simulation studies anddemonstrate its implementation using data from a multi-center randomized trial.

Efficient and robust methods for causally interpretable meta-analysis:  transporting inferences from multiple randomized trials to a target  population