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Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns

Sheng Li, Nikos Vlassis, Jaya Kawale, Yun Fu

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3768-3773.

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.

Sheng Li, Nikos Vlassis, Jaya Kawale, and Yun Fu. 2016. Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 3768–3773. https://ijcai.org/proceedings/2016/530
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