Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

• URL: http://arxiv.org/abs/2403.14232v1
• Date: Thu, 21 Mar 2024 08:41:53 GMT
• Title: Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
• Authors: Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang,
• Abstract summary: Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. We propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves.
• Score: 34.20279432270329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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