文章基本信息

标题：反事実伝播: 介入効果推定のための半教師付き学習
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作者：原田将之介 ; 鹿島久嗣
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2022
卷号：37
期号：3
页码：1-14
DOI：10.1527/tjsai.37-3_B-LA3
语种：Japanese
出版社：The Japanese Society for Artificial Intelligence
摘要：Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains. However, its estimation problem is difficult because intervention studies to collect information regarding the applied treatments (i.e., actions) and their outcomes are often quite expensive in terms of time and monetary costs. In this study, we consider a semi-supervised ITE estimation problem that exploits more easily-available unlabeled instances to improve the performance of ITE estimation using small labeled data. We combine two ideas from causal inference and semi-supervised learning, namely, matching and label propagation, respectively, to propose Counterfactual Propagation; CP which is the first semi-supervised ITE estimation method. Experiments using semi-real datasets demonstrate that the proposed method can successfully mitigate the data scarcity problem in ITE estimation.
关键词：causal Inference;treatment effect estimation;semi-supervised learning