  Policymakers often require information on programs' long-term impacts that is
not available when decisions are made. We demonstrate how data fusion methods
may be used address the problem of missing final outcomes and predict long-run
impacts of interventions before the requisite data are available. We implement
this method by concatenating data on an intervention with auxiliary long-term
data and then imputing missing long-term outcomes using short-term surrogate
outcomes while approximating uncertainty with replication methods. We use
simulations to examine the performance of the methodology and apply the method
in a case study. Specifically, we fuse data on the Oregon Health Insurance
Experiment with data from the National Longitudinal Mortality Study and
estimate that being eligible to apply for subsidized health insurance will lead
to a statistically significant improvement in long-term mortality.
