# Optimal treatment regimes for the net benefit of a treatment

arXiv: https://arxiv.org/abs/2503.22580 · HTML: https://mlsanity.com/paper/2503.22580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Preference-based Conditional Treatment Effects and Policy Learning](https://mlsanity.com/paper/2602.03823/) (2026)
- [Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes](https://mlsanity.com/paper/2610.03824/) (2026)

## Abstract (arXiv)

We develop a mathematical framework to define an optimal individualized treatment rule (ITR) within the context of prioritized outcomes in a randomized controlled trial. Our optimality criterion is based on the framework of generalized pairwise comparisons. We propose two approaches for estimating optimal ITRs on a pairwise basis. The first approach is a variant of the k-nearest neighbors algorithm. The second approach is a meta-learning method based on a randomized bagging scheme, which enables the use of any classification algorithm to construct an ITR. We investigate the theoretical properties of these estimation procedures, evaluate their performance through Monte Carlo simulations, and demonstrate their application to clinical trial data.