# Policy Learning with Confidence

arXiv: https://arxiv.org/abs/2502.10653 · HTML: https://mlsanity.com/paper/2502.10653/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Winner's Curse Drives False Promises in Data-Driven Decisions: A Case Study in Refugee Matching](https://mlsanity.com/paper/2602.08892/) (2026)
- [Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients](https://mlsanity.com/paper/2605.16593/) (2026)
- [A Hierarchy of Policy Learning Problems](https://mlsanity.com/paper/2607.03385/) (2026)

## Abstract (arXiv)

This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware rules on the efficient decision frontier, characterized as policies offering maximal estimated welfare for a given level of estimation risk. Among this class, the proposed rule is chosen to provide a reporting guarantee, ensuring that the welfare delivered exceeds a threshold with a pre-specified confidence level. We apply this approach to the allocation of a limited budget among social programs using estimates of their marginal value of public funds and associated standard errors.