Adapting Neural Networks for Uplift Models

Belbahri, Mouloud; Gandouet, Olivier; Kazma, Ghaith

doi:10.48550/arxiv.2011.00041

2020

DOI: 10.48550/arxiv.2011.00041

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Adapting Neural Networks for Uplift Models

Mouloud Belbahri¹,

Olivier Gandouet²,

Ghaith Kazma³

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2023

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“…Recently, technology companies have been using ITE estimation to optimize their marketing efforts [10,39,40,42,43]. There is a growing trend towards using statistical and machine learning methods to estimate ITEs from observational data [1,6,11,21,25,27,[32][33][34]. However, most existing research has focused on the conditional average treatment effect (CATE), which is the average individual treatment effect given certain baseline characteristics.…”

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confidence: 99%

DNet: Distributional Network for Distributional Individualized Treatment Effects

Wu¹,

G²,

Lv³

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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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. CCS CONCEPTS• Computing methodologies → Machine learning algorithms.

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confidence: 99%

DNet: Distributional Network for Distributional Individualized Treatment Effects

Wu¹,

G²,

Lv³

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

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Adapting Neural Networks for Uplift Models

Cited by 1 publication

References 22 publications

DNet: Distributional Network for Distributional Individualized Treatment Effects

DNet: Distributional Network for Distributional Individualized Treatment Effects

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