  We show how entropy balancing can be used for transporting experimental
treatment effects from a trial population onto a target population. This method
is doubly-robust in the sense that if either the outcome model or the
probability of trial participation is correctly specified, then the estimate of
the target population average treatment effect is consistent. Furthermore, we
only require the sample moments of the effect modifiers drawn from the target
population to consistently estimate the target population average treatment
effect. We compared the finite-sample performance of entropy balancing with
several alternative methods for transporting treatment effects between
populations. Entropy balancing techniques are efficient and robust to
violations of model misspecification. We also examine the results of our
proposed method in an applied analysis of the Action to Control Cardiovascular
Risk in Diabetes Blood Pressure (ACCORD-BP) trial transported to a sample of US
adults with diabetes taken from the National Health and Nutrition Examination
Survey (NHANES) cohort.
