Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.

翻译：因果推断在电子商务和精准医学等多个领域具有广泛应用，其性能高度依赖于个体处理效应（ITE）的准确估计。传统上，ITE是通过分别在处理组和对照组样本空间中建模响应函数来预测的。然而，这种方法在实践中常面临两个问题：处理偏差导致的处理组与对照组分布差异，以及两者群体规模的显著样本不平衡。本文提出深度全空间交叉网络（DESCN），从端到端视角建模处理效应。DESCN通过多任务学习方式，利用交叉网络捕捉处理倾向性、响应函数以及隐藏处理效应的整合信息。我们的方法在全样本空间中联合学习处理函数与响应函数以消除处理偏差，并引入中间伪处理效应预测网络缓解样本不平衡问题。在合成数据集和电子商务优惠券分发业务的大规模生产数据集上进行了广泛实验。结果表明，DESCN能够有效提升ITE估计的准确性和提升排名性能。为促进社区未来研究，我们公开发布了生产数据集样本和源代码——据我们所知，这是首个面向因果推断的大规模公开偏差处理数据集。