Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information

• URL: http://arxiv.org/abs/2209.01956v1
• Date: Mon, 5 Sep 2022 13:20:12 GMT
• Title: Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
• Authors: Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang
• Abstract summary: Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. We propose a moderately-balanced representation learning framework. This framework protects the representation from being over-balanced via multi-task learning.
• Score: 14.040918087553177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is "orthogonal" to be more robust. The others explore representation learning models to achieve a balanced representation between the treated and the controlled groups. However, existing studies fail to 1) discriminate treated units from controlled ones in the representation space to avoid the over-balanced issue; 2) fully utilize the "orthogonality information". In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory. This framework protects the representation from being over-balanced via multi-task learning. Simultaneously, MBRL incorporates the noise orthogonality information in the training and validation stages to achieve a better ATE estimation. The comprehensive experiments on benchmark and simulated datasets show the superiority and robustness of our method on treatment effect estimations compared with existing state-of-the-art methods.

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