# More Efficient Policy Learning via Optimal Retargeting

arXiv: https://arxiv.org/abs/1906.08611 · HTML: https://mlsanity.com/paper/1906.08611/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Reliable Off-Policy Learning for Dosage Combinations](https://mlsanity.com/paper/2305.19742/) (2023)
- [Fair Off-Policy Learning from Observational Data](https://mlsanity.com/paper/2303.08516/) (2023)
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- [Learning Complementary Policies for Human-AI Teams](https://mlsanity.com/paper/2302.02944/) (2023)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [Efficient and Sharp Off-Policy Learning under Unobserved Confounding](https://mlsanity.com/paper/2502.13022/) (2025)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [Balanced Off-Policy Evaluation for Personalized Pricing](https://mlsanity.com/paper/2302.12736/) (2023)
- [The Value of Prediction in Identifying the Worst-Off](https://mlsanity.com/paper/2501.19334/) (2025)
- [Confounding-Robust Policy Improvement with Human-AI Teams](https://mlsanity.com/paper/2310.08824/) (2023)
- [Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Population](https://mlsanity.com/paper/2105.00581/) (2021)
- [Constrained Classification and Policy Learning](https://mlsanity.com/paper/2106.12886/) (2021)
- [Transfer Q-learning](https://mlsanity.com/paper/2202.04709/) (2022)
- [Boosted Off-Policy Learning](https://mlsanity.com/paper/2208.01148/) (2022)
- [Practical Performative Policy Learning with Strategic Agents](https://mlsanity.com/paper/2412.01344/) (2024)
- [Rank-Learner: Orthogonal Ranking of Treatment Effects](https://mlsanity.com/paper/2602.03517/) (2026)
- [Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome](https://mlsanity.com/paper/2104.10554/) (2021)
- [Counterfactual Learning with Multioutput Deep Kernels](https://mlsanity.com/paper/2211.11119/) (2022)
- [MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability](https://mlsanity.com/paper/2504.20908/) (2025)
- [Functional Natural Policy Gradients](https://mlsanity.com/paper/2603.28681/) (2026)
- [Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints](https://mlsanity.com/paper/2106.05031/) (2021)
- [Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules](https://mlsanity.com/paper/2110.08936/) (2021)

## Abstract (arXiv)

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.