Academic Journal
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
| العنوان: | Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation |
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| المؤلفون: | Zhu, Minqin, Wu, Anpeng, Li, Haoxuan, Xiong, Ruoxuan, Li, Bo, Yang, Xiaoqing, Qin, Xuan, Zhen, Peng, Guo, Jiecheng, Wu, Fei, Kuang, Kun |
| المصدر: | Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38 No. 15: AAAI-24 Technical Tracks 15; 17175-17183 ; 2374-3468 ; 2159-5399 |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence |
| سنة النشر: | 2024 |
| المجموعة: | Association for the Advancement of Artificial Intelligence: AAAI Publications |
| مصطلحات موضوعية: | ML: Causal Learning, ML: Representation Learning |
| الوصف: | Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | https://ojs.aaai.org/index.php/AAAI/article/view/29663/31131; https://ojs.aaai.org/index.php/AAAI/article/view/29663/31132; https://ojs.aaai.org/index.php/AAAI/article/view/29663 |
| DOI: | 10.1609/aaai.v38i15.29663 |
| الاتاحة: | https://ojs.aaai.org/index.php/AAAI/article/view/29663 https://doi.org/10.1609/aaai.v38i15.29663 |
| Rights: | Copyright (c) 2024 Association for the Advancement of Artificial Intelligence |
| رقم الانضمام: | edsbas.F2EFC9A1 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1609/aaai.v38i15.29663 |
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