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DNet: distributional network for distributional individualized treatment effects

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Listed:
  • Wu, Guojun
  • Song, Ge
  • Lv, Xiaoxiang
  • Luo, Shikai
  • Shi, Chengchun
  • Zhu, Hongtu

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.

Suggested Citation

  • Wu, Guojun & Song, Ge & Lv, Xiaoxiang & Luo, Shikai & Shi, Chengchun & Zhu, Hongtu, 2023. "DNet: distributional network for distributional individualized treatment effects," LSE Research Online Documents on Economics 122895, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:122895
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    References listed on IDEAS

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    More about this item

    Keywords

    uplift modeling; causal inference; quantile regression;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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