{"type":"ScholarlyArticle","title":"On the Estimation of Treatment Effect with Text Covariates","authors":[{"name":"Liuyi Yao","slug":"liuyi-yao","given":"Liuyi","family":"Yao","affiliation":"University at Buffalo"},{"name":"Sheng Li","slug":"sheng-li","given":"Sheng","family":"Li","affiliation":"University of Georgia"},{"name":"Yaliang Li","slug":"yaliang-li","given":"Yaliang","family":"Li","affiliation":"Alibaba Group"},{"name":"Hongfei Xue","slug":"hongfei-xue","given":"Hongfei","family":"Xue","affiliation":"University at Buffalo"},{"name":"Jing Gao","slug":"jing-gao","given":"Jing","family":"Gao","affiliation":"University at Buffalo"},{"name":"Aidong Zhang","slug":"aidong-zhang","given":"Aidong","family":"Zhang","affiliation":"University of Virginia"}],"abstract":"Estimating the treatment effect benefits decision making in various domains as it can provide the potential outcomes of different choices. Existing work mainly focuses on covariates with numerical values, while how to handle covariates with textual information for treatment effect estimation is still an open question. One major challenge is how to filter out the nearly instrumental variables which are the variables more predictive to the treatment than the outcome. Conditioning on those variables to estimate the treatment effect would amplify the estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out the information related to nearly instrumental variables when learning the representations, and then it performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce the bias of treatment effect estimation, which is demonstrated by our experimental results on both semi-synthetic and real-world datasets.","doi":"10.24963/ijcai.2019/570","url":"https://ijcai.org/proceedings/2019/570","pdf":"https://ijcai.org/proceedings/2019/0570.pdf","markdown":"https://ijcai.org/proceedings/2019/570.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/570","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":570,"pages":{"first":4106,"last":4113},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Brooks-Gunn et al., 1992] Jeanne Brooks-Gunn, Fong-ruey Liaw, and Pamela Kato Klebanov. 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