# Optimal Decision Rules Under Partial Identification

arXiv: https://arxiv.org/abs/2111.04926 · HTML: https://mlsanity.com/paper/2111.04926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2021.

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

- [Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War](https://mlsanity.com/paper/2307.08840/) (2023)
- [When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning](https://mlsanity.com/paper/2608.02940/) (2026)
- [Same Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures](https://mlsanity.com/paper/2609.34215/) (2026)

## Abstract (arXiv)

I consider a class of statistical decision problems in which the policymaker must decide between two policies to maximize social welfare (e.g., the population mean of an outcome) based on a finite sample. The framework introduced in this paper allows for various types of restrictions on the structural parameter (e.g., the smoothness of a conditional mean potential outcome function) and accommodates settings with partial identification of social welfare. As the main theoretical result, I derive a finite-sample optimal decision rule under the minimax regret criterion. This rule has a simple form, yet achieves optimality among all decision rules; no ad hoc restrictions are imposed on the class of decision rules. I apply my results to the problem of whether to change an eligibility cutoff in a regression discontinuity setup, and illustrate them in an empirical application to a school construction program in Burkina Faso.