  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.
