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.
Commerce; Medicines; Education.
| 氏名 | コース | 研究室 | 役職/学年 |
|---|---|---|---|
| Xiaofeng Lin | 知能情報学コース | Machine Learning and Data Mining Research Laboratory (鹿島研究室) | 博士1回生 |
| Guoxi Zhang | その他の専攻・大学 | その他: その他 | |
| Xiaotian Lu | 知能情報学コース | Machine Learning and Data Mining Research Laboratory (鹿島研究室) | 博士2回生 |
| Han Bao | 知能情報学コース | Machine Learning and Data Mining Research Laboratory (鹿島研究室) | 特定助教 |
| 竹内孝 | 知能情報学コース | Machine Learning and Data Mining Research Laboratory (鹿島研究室) | 講師 |
| 鹿島久嗣 | 知能情報学コース | Machine Learning and Data Mining Research Laboratory (鹿島研究室) | 教授 |