# Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data arXiv: https://arxiv.org/abs/2412.11136 ยท HTML: https://mlsanity.com/paper/2412.11136/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 72 references that are in the map. - First posted on arXiv 15 Dec 2024. ## Builds on (most cited first, 30 of 38) - [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) - [Learning Models with Uniform Performance via Distributionally Robust Optimization](https://mlsanity.com/paper/1810.08750/) (2018) - [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017) - [Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach](https://mlsanity.com/paper/1610.03425/) (2016) - [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021) - [Distributionally Robust Federated Averaging](https://mlsanity.com/paper/2102.12660/) (2021) - [Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho](https://mlsanity.com/paper/2108.02836/) (2021) - [Confounding-Robust Policy Improvement](https://mlsanity.com/paper/1805.08593/) (2018) - [Bounds on the conditional and average treatment effect with unobserved confounding factors](https://mlsanity.com/paper/1808.09521/) (2018) - [Maximin effects in inhomogeneous large-scale data](https://mlsanity.com/paper/1406.0596/) (2014) - [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022) - [Minimax Regret Optimization for Robust Machine Learning under Distribution Shift](https://mlsanity.com/paper/2202.05436/) (2022) - [Learning Optimal Distributionally Robust Individualized Treatment Rules](https://mlsanity.com/paper/2006.15121/) (2020) - [Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding](https://mlsanity.com/paper/2112.11449/) (2021) - [Adaptive Combination of Randomized and Observational Data](https://mlsanity.com/paper/2111.15012/) (2021) - [Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation](https://mlsanity.com/paper/2005.10579/) (2020) - [Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies](https://mlsanity.com/paper/2011.07568/) (2020) - [A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources](https://mlsanity.com/paper/2103.06261/) (2021) - [Optimal Multi-Distribution Learning](https://mlsanity.com/paper/2312.05134/) (2023) - [Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective](https://mlsanity.com/paper/2203.04373/) (2022) - [Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population](https://mlsanity.com/paper/1908.09230/) (2019) - [Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment](https://mlsanity.com/paper/2109.11679/) (2021) - [Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity](https://mlsanity.com/paper/2302.13428/) (2023) - [Policy Learning under Biased Sample Selection](https://mlsanity.com/paper/2304.11735/) (2023) - [Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations](https://mlsanity.com/paper/2112.04723/) (2021) - [Minimax Regret Learning for Data with Heterogeneous Subgroups](https://mlsanity.com/paper/2405.01709/) (2024) - [Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects](https://mlsanity.com/paper/1610.06943/) (2016) - [Sensitivity Analysis in the Generalization of Experimental Results](https://mlsanity.com/paper/2202.03408/) (2022) ## Built on by (most cited first, 5 of 5) - [Maximum Risk Minimization with Random Forests](https://mlsanity.com/paper/2512.10445/) (2025) - [Doubly Robust Fusion of Many Treatments for Policy Learning](https://mlsanity.com/paper/2505.08092/) (2025) - [CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets](https://mlsanity.com/paper/2508.11144/) (2025) - [Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data](https://mlsanity.com/paper/2603.05568/) (2026) - [Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes](https://mlsanity.com/paper/2603.27114/) (2026) ## Abstract (arXiv) To test scientific theories and develop individualized treatment rules, researchers often wish to learn heterogeneous treatment effects that can be consistently found across diverse populations and contexts. We consider the problem of generalizing heterogeneous treatment effects (HTE) based on data from multiple sites. A key challenge is that a target population may differ from the source sites in unknown and unobservable ways. This means that the estimates from site-specific models lack external validity, and a simple pooled analysis risks bias. We develop a robust CATE (conditional average treatment effect) estimation methodology with multisite data from heterogeneous populations. We propose a minimax-regret framework that learns a generalizable CATE model by minimizing the worst-case regret over a class of target populations whose CATE can be represented as convex combinations of site-specific CATEs. Using robust optimization, the proposed methodology accounts for distribution shifts in both individual covariates and treatment effect heterogeneity across sites. We show that the resulting CATE model has an interpretable closed-form solution, expressed as a weighted average of site-specific CATE models. Thus, researchers can utilize a flexible CATE estimation method within each site and aggregate site-specific estimates to produce the final model. Through simulations and a real-world application, we show that the proposed methodology improves the robustness and generalizability of existing approaches.