  Counterfactual predictions are challenging when the policy variable goes
beyond its pre-policy support. However, in many cases, information about the
policy of interest is available from different ("source") regions where a
similar policy has already been implemented. In this paper, we propose a novel
method of using such data from source regions to predict a new policy in a
target region. Instead of relying on extrapolation of a structural relationship
using a parametric specification, we formulate a transferability condition and
construct a synthetic outcome-policy relationship such that it is as close as
possible to meeting the condition. The synthetic relationship weighs both the
similarity in distributions of observables and in structural relationships. We
develop a general procedure to construct asymptotic confidence intervals for
counterfactual predictions and prove its asymptotic validity. We then apply our
proposal to predict average teenage employment in Texas following a
counterfactual increase in the minimum wage.
