  Post-click conversion rate (CVR) prediction is an essential task for
discovering user interests and increasing platform revenues in a range of
industrial applications. One of the most challenging problems of this task is
the existence of severe selection bias caused by the inherent self-selection
behavior of users and the item selection process of systems. Currently, doubly
robust (DR) learning approaches achieve the state-of-the-art performance for
debiasing CVR prediction. However, in this paper, by theoretically analyzing
the bias, variance and generalization bounds of DR methods, we find that
existing DR approaches may have poor generalization caused by inaccurate
estimation of propensity scores and imputation errors, which often occur in
practice. Motivated by such analysis, we propose a generalized learning
framework that not only unifies existing DR methods, but also provides a
valuable opportunity to develop a series of new debiasing techniques to
accommodate different application scenarios. Based on the framework, we propose
two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the
bias of DR loss, while DR-MSE balances the bias and variance flexibly, which
achieves better generalization performance. In addition, we propose a novel
tri-level joint learning optimization method for DR-MSE in CVR prediction, and
an efficient training algorithm correspondingly. We conduct extensive
experiments on both real-world and semi-synthetic datasets, which validate the
effectiveness of our proposed methods.
