# Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes

arXiv: https://arxiv.org/abs/2603.27114 · HTML: https://mlsanity.com/paper/2603.27114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021)
- [Maximin effects in inhomogeneous large-scale data](https://mlsanity.com/paper/1406.0596/) (2014)
- [Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies](https://mlsanity.com/paper/2011.07568/) (2020)
- [Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference](https://mlsanity.com/paper/2508.17099/) (2025)
- [A Note on Exploratory Item Factor Analysis by Singular Value Decomposition](https://mlsanity.com/paper/1907.08713/) (2019)
- [Minimax Regret Learning for Data with Heterogeneous Subgroups](https://mlsanity.com/paper/2405.01709/) (2024)
- [Estimation and Optimization of Composite Outcomes](https://mlsanity.com/paper/1711.10581/) (2017)
- [Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data](https://mlsanity.com/paper/2412.11136/) (2024)
- [Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments](https://mlsanity.com/paper/2011.00094/) (2020)
- [Reduced-Rank Multi-objective Policy Learning and Optimization](https://mlsanity.com/paper/2404.18490/) (2024)

## Abstract (arXiv)

Precision mental health requires treatment decisions that account for heterogeneous symptoms reflecting multiple clinical domains. However, existing methods for estimating individualized treatment effects (ITE) rely on a single summary outcome or a specific set of observed symptoms or measures, which are sensitive to symptom selection and limit generalizability to unmeasured yet clinically relevant domains. We propose DRIFT, a new maximin framework for estimating robust ITEs from high-dimensional item-level data by leveraging latent factor representations and adversarial learning. DRIFT learns latent constructs via generalized factor analysis, then constructs an anchored on-target uncertainty set that extrapolates beyond the observed measures to approximate the broader hyper-population of potential outcomes. By optimizing worst-case performance over this uncertainty set, DRIFT yields ITEs that are robust to underrepresented or unmeasured domains. We further show that DRIFT is invariant to admissible reparameterizations of the latent factors and admits a closed-form maximin solution, with theoretical guarantees for identification and convergence. In analyses of a randomized controlled trial for major depressive disorder (EMBARC), DRIFT demonstrates superior performance and improved generalizability to external multi-domain outcomes, including side effects and self-reported symptoms not used during training.