Transporting Experimental Results with Entropy Balancing
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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 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. We found that entropy balancing is more robust to violations of the positivity assumption relative to the other methods while remaining efficient in each of the scenarios we tested. 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.
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