GraphITE: Estimating Individual Effects of Graph-structured Treatments

• URL: http://arxiv.org/abs/2009.14061v3
• Date: Sun, 12 Sep 2021 11:41:04 GMT
• Title: GraphITE: Estimating Individual Effects of Graph-structured Treatments
• Authors: Shonosuke Harada and Hisashi Kashima
• Abstract summary: In some applications, the number of treatments can be significantly large. In this study, we consider the outcome estimation problem of graph-structured treatments such as drugs. Our proposed method, GraphITE, learns the representations of graph-structured treatments using graph neural networks.
• Score: 21.285425135761795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations. Most existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of treatments can be significantly large, while the treatments themselves have rich information. In this study, we considered one important instance of such cases: the outcome estimation problem of graph-structured treatments such as drugs. Owing to the large number of possible treatments, the counterfactual nature of observational data that appears in conventional treatment effect estimation becomes more of a concern for this problem. Our proposed method, GraphITE (pronounced "graphite") learns the representations of graph-structured treatments using graph neural networks while mitigating observation biases using Hilbert-Schmidt Independence Criterion regularization, which increases the independence of the representations of the targets and treatments. Experiments on two real-world datasets show that GraphITE outperforms baselines, especially in cases with a large number of treatments.

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