  Treatment effect estimation from observational data is a critical research
topic across many domains. The foremost challenge in treatment effect
estimation is how to capture hidden confounders. Recently, the growing
availability of networked observational data offers a new opportunity to deal
with the issue of hidden confounders. Unlike networked data in traditional
graph learning tasks, such as node classification and link detection, the
networked data under the causal inference problem has its particularity, i.e.,
imbalanced network structure. In this paper, we propose a Graph Infomax
Adversarial Learning (GIAL) model for treatment effect estimation, which makes
full use of the network structure to capture more information by recognizing
the imbalance in network structure. We evaluate the performance of our GIAL
model on two benchmark datasets, and the results demonstrate superiority over
the state-of-the-art methods.
