  Marketing campaigns are a set of strategic activities that can promote a
business's goal. The effect prediction for marketing campaigns in a real
industrial scenario is very complex and challenging due to the fact that prior
knowledge is often learned from observation data, without any intervention for
the marketing campaign. Furthermore, each subject is always under the
interference of several marketing campaigns simultaneously. Therefore, we
cannot easily parse and evaluate the effect of a single marketing campaign. To
the best of our knowledge, there are currently no effective methodologies to
solve such a problem, i.e., modeling an individual-level prediction task based
on a hierarchical structure with multiple intertwined events. In this paper, we
provide an in-depth analysis of the underlying parse tree-like structure
involved in the effect prediction task and we further establish a Hierarchical
Capsule Prediction Network (HapNet) for predicting the effects of marketing
campaigns. Extensive results based on both the synthetic data and real data
demonstrate the superiority of our model over the state-of-the-art methods and
show remarkable practicability in real industrial applications.
