  Randomized control trials (RCTs) are the gold standard for estimating causal
effects, but often use samples that are non-representative of the actual
population of interest. We propose a reweighting method for estimating
population average treatment effects in settings with noncompliance.
Simulations show the proposed compliance-adjusted population estimator
outperforms its unadjusted counterpart when compliance is relatively low and
can be predicted by observed covariates. We apply the method to evaluate the
effect of Medicaid coverage on health care use for a target population of
adults who may benefit from expansions to the Medicaid program. We draw RCT
data from the Oregon Health Insurance Experiment, where less than one-third of
those randomly selected to receive Medicaid benefits actually enrolled.
