{"type":"ScholarlyArticle","title":"Three-quarter Sibling Regression for Denoising Observational Data","authors":[{"name":"Shiv Shankar","slug":"shiv-shankar","given":"Shiv","family":"Shankar","affiliation":"University of Massachusetts Amherst"},{"name":"Daniel Sheldon","slug":"daniel-sheldon","given":"Daniel","family":"Sheldon","affiliation":"University of Massachusetts Amherst; Mount Holyoke College"},{"name":"Tao Sun","slug":"tao-sun","given":"Tao","family":"Sun","affiliation":""},{"name":"John Pickering","slug":"john-pickering","given":"John","family":"Pickering","affiliation":"University of Georgia"},{"name":"Thomas G. Dietterich","slug":"thomas-g-dietterich","given":"Thomas G.","family":"Dietterich","affiliation":"Oregon State University"}],"abstract":"Many ecological studies and conservation policies are based on field observations of species, which can be affected by systematic variability introduced by the observation process. A recently introduced causal modeling technique called 'half-sibling regression' can detect and correct for systematic errors in measurements of multiple independent random variables. However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes. We present a technique called 'three-quarter sibling regression' to partially overcome this limitation. It can filter the effect of systematic noise when the latent variables have observed common causes. We provide theoretical justification of this approach, demonstrate its effectiveness on synthetic data, and show that it reduces systematic detection variability due to moon brightness in moth surveys.","doi":"10.24963/ijcai.2019/826","url":"https://ijcai.org/proceedings/2019/826","pdf":"https://ijcai.org/proceedings/2019/0826.pdf","markdown":"https://ijcai.org/proceedings/2019/826.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/826","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":826,"pages":{"first":5960,"last":5966},"keywords":["Special Track on AI for Improving Human-Well Being: Computational sustainability (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Adams et al., 2019] Roy Adams, Yuelong Ji, Xiaobin Wang, and Suchi Saria. 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