DNet: distributional network for distributional individualized treatment effects

Wu, Guojun, Song, Ge, Lv, Xiaoxiang, Luo, Shikai, Shi, Chengchun and Zhu, Hongtu (2023) DNet: distributional network for distributional individualized treatment effects. In: 2023 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2023-08-06 - 2023-08-10, Long Beach, California, United States, USA.

Text (Distributional Network for Distributional Individualized Treatment Effects) - Published Version Available under License Other license. Download (661kB)

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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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© The Authors | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 2023 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, http://dx.doi.org/10.1145/3580305.3599809
Divisions:	Statistics
Date Deposited:	03 May 2024 11:48
Last Modified:	16 May 2024 11:14
URI:	http://eprints.lse.ac.uk/id/eprint/122895

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