  Post-click conversion rate (CVR) estimation is a critical task in e-commerce
recommender systems. This task is deemed quite challenging under the industrial
setting with two major issues: 1) selection bias caused by user self-selection,
and 2) data sparsity due to the rare click events. A successful conversion
typically has the following sequential events: "exposure -> click ->
conversion". Conventional CVR estimators are trained in the click space, but
the inference is done in the entire exposure space. They fail to account for
the causes of the missing data and treat them as missing at random. Hence,
their estimations are highly likely to deviate from the real values by large.
In addition, the data sparsity issue can also handicap many industrial CVR
estimators which usually have large parameter spaces.
  In this paper, we propose two principled, efficient and highly effective CVR
estimators for industrial CVR estimation, namely, Multi-IPW and Multi-DR. The
proposed models approach the CVR estimation from a causal perspective and
account for the causes of missing not at random. In addition, our methods are
based on the multi-task learning framework and mitigate the data sparsity
issue. Extensive experiments on industrial-level datasets show that our methods
outperform the state-of-the-art CVR models.
