  Policy learning can be used to extract individualized treatment regimes from
observational data in healthcare, civics, e-commerce, and beyond. One big
hurdle to policy learning is a commonplace lack of overlap in the data for
different actions, which can lead to unwieldy policy evaluation and poorly
performing learned policies. We study a solution to this problem based on
retargeting, that is, changing the population on which policies are optimized.
We first argue that at the population level, retargeting may induce little to
no bias. We then characterize the optimal reference policy and retargeting
weights in both binary-action and multi-action settings. We do this in terms of
the asymptotic efficient estimation variance of the new learning objective.
Extensive empirical results in a simulation study and a case study of
personalized job counseling demonstrate that retargeting is a fairly easy way
to significantly improve any policy learning procedure applied to observational
data.
