{"type":"ScholarlyArticle","title":"CounterFactual Regression with Importance Sampling Weights","authors":[{"name":"Negar Hassanpour","slug":"negar-hassanpour","given":"Negar","family":"Hassanpour","affiliation":"Department of Computing Science , University of Alberta , Canada"},{"name":"Russell Greiner","slug":"russell-greiner","given":"Russell","family":"Greiner","affiliation":"Department of Computing Science , University of Alberta , Canada"}],"abstract":"Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under various treatment options. For a binary-treatment case, this translates into estimating the difference between the outcomes (e.g., survival time) of the two available treatment options – i.e., her Individual Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data has selection bias: the treatment assigned to a patient depends on that patient's attributes. In this work, we borrow ideas from domain adaptation to address the distributional shift between the source (outcome of the administered treatment, appearing in the observed training data) and target (outcome of the alternative treatment) that exists due to selection bias. We propose a context-aware importance sampling re-weighing scheme, built on top of a representation learning module, for estimating ITEs. Empirical results on two publicly available benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art.","doi":"10.24963/ijcai.2019/815","url":"https://ijcai.org/proceedings/2019/815","pdf":"https://ijcai.org/proceedings/2019/0815.pdf","markdown":"https://ijcai.org/proceedings/2019/815.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/815","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":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":815,"pages":{"first":5880,"last":5887},"keywords":["Special Track on AI for Improving Human-Well Being: Health applications (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Atan et al., 2018] Onur Atan, James Jordon, and Mihaela van der Schaar. 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