ArticleProceedings of machine learning research2022
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 9 citations in OpenAlex.
- Article
- Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data.Biometrics · 2026Article
- Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning.Biostatistics (Oxford, England) · 2025Article
- Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring.IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society · 2024Article
- Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.Statistics in medicine · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.Journal of biomedical informatics · 2023Article
- FedDNA: Federated learning using dynamic node alignment.PloS one · 2023Article
- Meta-analysis of individualized treatment rules via sign-coherency.Proceedings of machine learning research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and lack of resources prevent a site from leveraging subject-level data from other sites. We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data. To our best knowledge, there is no established model averaging approach for distributed data with a focus on improving the estimation of treatment effects. Specifically, under distributed data networks, our framework provides an interpretable tree-based ensemble of CATE estimators that joins models across study sites, while actively modeling the heterogeneity in data sources through site partitioning. The performance of this approach is demonstrated by a real-world study of the causal effects of oxygen therapy on hospital survival rate and backed up by comprehensive simulation results.
Identifiers
38084268PMC10711748W4287274167What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.