# Selecting optimal subgroups for treatment using many covariates

arXiv: https://arxiv.org/abs/1802.09642 · HTML: https://mlsanity.com/paper/1802.09642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search](https://mlsanity.com/paper/2110.05636/) (2021)
- [A framework for causal segmentation analysis with machine learning in large-scale digital experiments](https://mlsanity.com/paper/2111.01223/) (2021)
- [Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data](https://mlsanity.com/paper/2412.19711/) (2024)
- [MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability](https://mlsanity.com/paper/2504.20908/) (2025)

## Abstract (arXiv)

We consider the problem of selecting the optimal subgroup to treat when data on covariates is available from a randomized trial or observational study. We distinguish between four different settings including (i) treatment selection when resources are constrained, (ii) treatment selection when resources are not constrained, (iii) treatment selection in the presence of side effects and costs, and (iv) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios but the form of the optimal treatment selection rule does not. The results suggest a move away from traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in health care practice.