DNet: distributional network for distributional individualized treatment effects

Guojun Wu, Ge Song, Xiaoxiang Lv, Shikai Luo, Chengchun Shi and Hongtu Zhu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: There is a growing interest in developing methods to estimate individualized treatment effects (ITEs) for various real-world applications, such as e-commerce and public health. This paper presents a novel architecture, called DNet, to infer distributional ITEs. DNet can learn the entire outcome distribution for each treatment, whereas most existing methods primarily focus on the conditional average treatment effect and ignore the conditional variance around its expectation. Additionally, our method excels in settings with heavy-tailed outcomes and outperforms state-of-the-art methods in extensive experiments on benchmark and real-world datasets. DNet has also been successfully deployed in a widely used mobile app with millions of daily active users.

Keywords: uplift modeling; causal inference; quantile regression (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2023-08-04
New Economics Papers: this item is included in nep-ecm
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Published in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4, August, 2023, 2023, pp. 5215 - 5224. ISSN: 2154-817X

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:122895

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