# Doubly Robust Fusion of Many Treatments for Policy Learning

arXiv: https://arxiv.org/abs/2505.08092 · HTML: https://mlsanity.com/paper/2505.08092/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 60 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 30 of 34)

- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Causal Effect Inference for Structured Treatments](https://mlsanity.com/paper/2106.01939/) (2021)
- [The Impact of Random Models on Clustering Similarity](https://mlsanity.com/paper/1701.06508/) (2017)
- [Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling](https://mlsanity.com/paper/2305.08062/) (2023)
- [Learning Optimal Distributionally Robust Individualized Treatment Rules](https://mlsanity.com/paper/2006.15121/) (2020)
- [Semi-Parametric Efficient Policy Learning with Continuous Actions](https://mlsanity.com/paper/1905.10116/) (2019)
- [Conformal Off-Policy Prediction in Contextual Bandits](https://mlsanity.com/paper/2206.04405/) (2022)
- [Augmented balancing weights as linear regression](https://mlsanity.com/paper/2304.14545/) (2023)
- [Zero-shot causal learning](https://mlsanity.com/paper/2301.12292/) (2023)
- [A concave pairwise fusion approach to subgroup analysis](https://mlsanity.com/paper/1508.07045/) (2015)
- [Reliable Off-Policy Learning for Dosage Combinations](https://mlsanity.com/paper/2305.19742/) (2023)
- [Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings](https://mlsanity.com/paper/2010.15963/) (2020)
- [Conformal Off-policy Prediction](https://mlsanity.com/paper/2206.06711/) (2022)
- [Off-Policy Evaluation for Large Action Spaces via Policy Convolution](https://mlsanity.com/paper/2310.15433/) (2023)
- [Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions](https://mlsanity.com/paper/2303.14226/) (2023)
- [Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes](https://mlsanity.com/paper/1504.07715/) (2015)
- [Nonparametric inverse probability weighted estimators based on the highly adaptive lasso](https://mlsanity.com/paper/2005.11303/) (2020)
- [Improving trial generalizability using observational studies](https://mlsanity.com/paper/2003.01242/) (2020)
- [Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis](https://mlsanity.com/paper/2201.01357/) (2022)
- [Optimal Individualized Treatment Rule for Combination Treatments Under Budget Constraints](https://mlsanity.com/paper/2303.11507/) (2023)
- [Bayesian Off-Policy Evaluation and Learning for Large Action Spaces](https://mlsanity.com/paper/2402.14664/) (2024)
- [Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data](https://mlsanity.com/paper/2412.11136/) (2024)
- [Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research](https://mlsanity.com/paper/2001.09765/) (2020)
- [Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score](https://mlsanity.com/paper/2007.04445/) (2020)
- [Transfer learning of individualized treatment rules from experimental to real-world data](https://mlsanity.com/paper/2108.08415/) (2021)
- [High dimensional precision medicine from patient-derived xenografts](https://mlsanity.com/paper/1912.06667/) (2019)
- [Learning Robust Decision Policies from Observational Data](https://mlsanity.com/paper/2006.02355/) (2020)
- [Robust Offline Policy Learning with Observational Data from Multiple Sources](https://mlsanity.com/paper/2410.08537/) (2024)
- [Targeted Optimal Treatment Regime Learning Using Summary Statistics](https://mlsanity.com/paper/2201.06229/) (2022)

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

- [Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality](https://mlsanity.com/paper/2605.01907/) (2026)
- [Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments](https://mlsanity.com/paper/2610.19104/) (2026)

## Abstract (arXiv)

Individualized treatment rules/recommendations (ITRs) aim to improve patient outcomes by tailoring treatments to the characteristics of each individual. However, when there are many treatment groups, existing methods face significant challenges due to data sparsity within treatment groups and highly unbalanced covariate distributions across groups. To address these challenges, we propose a novel calibration-weighted treatment fusion procedure that robustly balances covariates across treatment groups and fuses similar treatments using a penalized working model. The fusion procedure ensures the recovery of latent treatment group structures when either the calibration model or the outcome model is correctly specified. In the fused treatment space, practitioners can seamlessly apply state-of-the-art ITR learning methods with the flexibility to utilize a subset of covariates, thereby achieving robustness while addressing practical concerns such as fairness. We establish theoretical guarantees, including consistency, the oracle property of treatment fusion, and regret bounds when integrated with multi-armed ITR learning methods such as policy trees. Simulation studies show superior group recovery and policy value compared to existing approaches. We illustrate the practical utility of our method using a nationwide electronic health record-derived de-identified database containing data from patients with Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma.