  Most of the existing methods for debaising in click-through rate (CTR)
prediction depend on an oversimplified assumption, i.e., the click probability
is the product of observation probability and relevance probability. However,
since there is a complicated interplay between these two probabilities, these
methods cannot be applied to other scenarios, e.g. query auto completion (QAC)
and route recommendation. We propose a general debiasing framework without
simplifying the relationships between variables, which can handle all scenarios
in CTR prediction. Simulation experiments show that: under the simplest
scenario, our method maintains a similar AUC with the state-of-the-art methods;
in other scenarios, our method achieves considerable improvements compared with
existing methods. Meanwhile, in online experiments, the framework also gains
significant improvements consistently.
