{"type":"ScholarlyArticle","title":"Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns","authors":[{"name":"Sheng Li","slug":"sheng-li-2","given":"Sheng","family":"Li","affiliation":""},{"name":"Nikos Vlassis","slug":"nikos-vlassis","given":"Nikos","family":"Vlassis","affiliation":""},{"name":"Jaya Kawale","slug":"jaya-kawale","given":"Jaya","family":"Kawale","affiliation":""},{"name":"Yun Fu","slug":"yun-fu","given":"Yun","family":"Fu","affiliation":""}],"abstract":"A widely used method for estimating counterfactuals and causal treatment effects from observational data is nearest-neighbor matching. This typically involves pairing each treated unit with its nearest-in-covariates control unit, and then estimating an average treatment effect from the set of matched pairs. Although straightforward to implement, this estimator is known to suffer from a bias that increases with the dimensionality of the covariate space, which can be undesirable in applications that involve high-dimensional data. To address this problem, we propose a novel estimator that first projects the data to a number of random linear subspaces, and it then estimates the median treatment effect by nearest-neighbor matching in each subspace. We empirically compute the mean square error of the proposed estimator using semi-synthetic data, and we demonstrate the method on real-world digital marketing campaign data. The results show marked improvement over baseline methods.","doi":null,"url":"https://ijcai.org/proceedings/2016/530","pdf":"https://ijcai.org/proceedings/2016/0530.pdf","markdown":"https://ijcai.org/proceedings/2016/530.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/530","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":530,"pages":{"first":3768,"last":3773},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}