More Efficient Policy Learning via Optimal Retargeting
This addresses a common bottleneck in policy learning for applications like healthcare and e-commerce, offering an incremental improvement to existing methods.
The paper tackles the problem of poor policy performance due to lack of overlap in observational data by proposing retargeting, which changes the population for optimization, and shows it significantly improves policy learning procedures in simulations and a case study on personalized job counseling.
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.