Doubly Robust Fusion of Many Treatments for Policy Learning
Authors: Ke Zhu, Jianing Chu, Ilya Lipkovich, Wenyu Ye, Shu Yang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 deidentified database containing data from patients with Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma. |
| Researcher Affiliation | Collaboration | 1Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A. 2Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, U.S.A. 3Amazon (This work was done prior to joining Amazon) 4Eli Lilly & Company, Indianapolis, IN 46285, U.S.A.. |
| Pseudocode | Yes | Algorithm 1: Calibration-Weighted Treatment Fusion Input: Data {(Xi, Ai, Yi)}n i=1. for a = 1, . . . , K do ... Algorithm 2: Cross-Fitted AIPW Policy Learning Input: Data {(Xi, Ai, Yi)}n i=1; Group mapping δ. Split the data into L folds. for l = 1, . . . , L do ... |
| Open Source Code | No | The paper does not provide an explicit statement or link for the availability of source code. |
| Open Datasets | No | The paper uses a "nationwide Flatiron Health electronic health record-derived deidentified database". While it cites related work on this database (Ma et al., 2020; Birnbaum et al., 2020), it does not provide concrete access information, a direct link, or a clear statement that this proprietary database is openly available for download. |
| Dataset Splits | Yes | Algorithm 2: Cross-Fitted AIPW Policy Learning... Split the data into L folds. for l = 1, . . . , L do... |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions "R package policytree" but does not specify a version number. No other specific software versions are provided. |
| Experiment Setup | Yes | As a baseline, we implemented the CAIPWL method (Zhou et al., 2023) without calibration weighting or fusion, using the default tuning in the R package policytree to learn a depth-3 policy tree. ... In the fusion step, fused lasso uses extended BIC (Chen & Chen, 2008) for model selection. Treatments are grouped if the Euclidean distance between their fused lasso estimates is less than 0.25. |