# More Efficient Policy Learning via Optimal Retargeting

## TL;DR

This paper introduces a retargeting approach to improve policy learning from observational data by changing the population focus, reducing bias, and enhancing policy performance across various applications.

## Contribution

It characterizes the optimal retargeting weights and reference policies to minimize estimation variance, providing a practical method to improve policy learning.

## Key findings

- Retargeting can significantly improve policy learning performance.
- The method reduces bias without introducing substantial new bias.
- Empirical results show improved policies in simulations and real-world case studies.

## Abstract

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.