Online marketing is critical for many industrial platforms and business
applications, aiming to increase user engagement and platform revenue by
identifying corresponding delivery-sensitive groups for specific incentives,
such as coupons and bonuses. As the scale and complexity of features in
industrial scenarios increase, deep uplift modeling (DUM) as a promising
technique has attracted increased research from academia and industry,
resulting in various predictive models. However, current DUM still lacks some
standardized benchmarks and unified evaluation protocols, which limit the
reproducibility of experimental results in existing studies and the practical
value and potential impact in this direction. In this paper, we provide an open
benchmark for DUM and present comparison results of existing models in a
reproducible and uniform manner. To this end, we conduct extensive experiments
on two representative industrial datasets with different preprocessing settings
to re-evaluate 13 existing models. Surprisingly, our experimental results show
that the most recent work differs less than expected from traditional work in
many cases. In addition, our experiments also reveal the limitations of DUM in
generalization, especially for different preprocessing and test distributions.
Our benchmarking work allows researchers to evaluate the performance of new
models quickly but also reasonably demonstrates fair comparison results with
existing models. It also gives practitioners valuable insights into often
overlooked considerations when deploying DUM. We will make this benchmarking
library, evaluation protocol, and experimental setup available on GitHub.

在线营销是许多工业平台和商业应用的关键，本文提出了一个用于深层提升建模技术的开放基准，并以可重复和统一的方式比较了现有模型的结果。结果显示，最新的工作在许多情况下与传统工作相比差异较小，并揭示了深层提升建模在泛化方面的局限性，特别是在不同的预处理和测试分布情况下。