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、精准医学等多个领域有着广泛应用，其性能高度依赖于个体处理效应（ITE）的准确估计。传统上，ITE通过在处理组和对照组的各自样本空间中分别建模响应函数来进行预测。然而，这种方法在实践中常遇到两个问题：一是由于处理偏差导致处理组与对照组分布不一致，二是两组群体规模存在显著样本不平衡。本文提出深度全空间交叉网络（DESCN），从端到端的角度建模处理效应。DESCN通过多任务学习方式，利用交叉网络捕捉处理倾向、响应和隐藏处理效应的集成信息。我们的方法在全样本空间中联合学习处理和响应函数，以避免处理偏差，并采用中间伪处理效应预测网络来缓解样本不平衡。我们在合成数据集和来自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估计的准确性并改善提升排名性能。我们公开发布了生产数据集样本和源代码，以促进该领域的未来研究，据我们所知，这是首个用于因果推断的大规模公开有偏处理数据集。