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Representation Learning for Treatment Effect Estimation from Observational Data
Liuyi Yao · Sheng Li · Yaliang Li · Mengdi Huai · Jing Gao · Aidong Zhang

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #116

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that is helpful. In this paper, we propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods.

Author Information

Liuyi Yao (State University of New York at Buffalo)
Sheng Li (University of Georgia)
Yaliang Li (Tencent Medical AI Lab)
Mengdi Huai (State University of New York at Buffalo)
Jing Gao (University at Buffalo)
Aidong Zhang (SUNY Buffalo)

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