# Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups

arXiv: https://arxiv.org/abs/2211.14671 · HTML: https://mlsanity.com/paper/2211.14671/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2022.

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

- [Automated Efficient Estimation using Monte Carlo Efficient Influence Functions](https://mlsanity.com/paper/2403.00158/) (2024)
- [Targeted Learning for Variable Importance](https://mlsanity.com/paper/2411.02221/) (2024)
- [Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift](https://mlsanity.com/paper/2608.06512/) (2026)

## Abstract (arXiv)

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this manuscript, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.