  Outcome estimation of treatments for target individuals is an important
foundation for decision making based on causal relations. Most existing outcome
estimation methods deal with binary or multiple-choice treatments; however, in
some applications, the number of treatments can be significantly large, while
the treatments themselves have rich information. In this study, we considered
one important instance of such cases: the outcome estimation problem of
graph-structured treatments such as drugs. Owing to the large number of
possible treatments, the counterfactual nature of observational data that
appears in conventional treatment effect estimation becomes more of a concern
for this problem. Our proposed method, GraphITE (pronounced "graphite") learns
the representations of graph-structured treatments using graph neural networks
while mitigating observation biases using Hilbert-Schmidt Independence
Criterion regularization, which increases the independence of the
representations of the targets and treatments. Experiments on two real-world
datasets show that GraphITE outperforms baselines, especially in cases with a
large number of treatments.
