  Treatment effect estimation can assist in effective decision-making in
e-commerce, medicine, and education. One popular application of this estimation
lies in the prediction of the impact of a treatment (e.g., a promotion) on an
outcome (e.g., sales) of a particular unit (e.g., an item), known as the
individual treatment effect (ITE). In many online applications, the outcome of
a unit can be affected by the treatments of other units, as units are often
associated, which is referred to as interference. For example, on an online
shopping website, sales of an item will be influenced by an advertisement of
its co-purchased item. Prior studies have attempted to model interference to
estimate the ITE accurately, but they often assume a homogeneous interference,
i.e., relationships between units only have a single view. However, in
real-world applications, interference may be heterogeneous, with multi-view
relationships. For instance, the sale of an item is usually affected by the
treatment of its co-purchased and co-viewed items. We hypothesize that ITE
estimation will be inaccurate if this heterogeneous interference is not
properly modeled. Therefore, we propose a novel approach to model heterogeneous
interference by developing a new architecture to aggregate information from
diverse neighbors. Our proposed method contains graph neural networks that
aggregate same-view information, a mechanism that aggregates information from
different views, and attention mechanisms. In our experiments on multiple
datasets with heterogeneous interference, the proposed method significantly
outperforms existing methods for ITE estimation, confirming the importance of
modeling heterogeneous interference.
