Estimation of conditional average treatment effects on distributed confidential data
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Abstract (arXiv)
The estimation of conditional average treatment effects (CATEs) is an important topic in many scientific fields. CATEs can be estimated with high accuracy if data distributed across multiple parties are centralized. However, it is difficult to aggregate such data owing to confidentiality or privacy concerns. To address this issue, we propose data collaboration double machine learning, a method for estimating CATE models using privacy-preserving fusion data constructed from distributed sources, and evaluate its performance through simulations. We make three main contributions. First, our method enables estimation and testing of semi-parametric CATE models without iterative communication on distributed data, providing robustness to model mis-specification compared to parametric approaches. Second, it enables collaborative estimation across different time points and parties by accumulating a knowledge base. Third, our method performs as well as or better than existing methods in simulations using synthetic, semi-synthetic, and real-world datasets.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 5 Feb 2024.
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18 of 41 references.
References · 18
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- 2021Accuracy and Privacy Evaluations of Collaborative Data Analysiscited by 13
6 more
- 2023Fairness and Privacy-Preserving in Federated Learning: A Surveycited by 12
- 2022Collaborative causal inference on distributed datacited by 10
- 2022Non-readily identifiable data collaboration analysis for multiple datasets including personal informationcited by 10
- 2020Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representationscited by 8
- 2020Double machine learning for sample selection modelscited by 8
- 2023Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matchingcited by 1
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