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Treatment effect estimation from observational data is a critical researchtopic across many domains. The foremost challenge in treatment effectestimation is how to capture hidden confounders. Recently, the growingavailability of networked observational data offers a new opportunity to dealwith the issue of hidden confounders. Unlike networked data in traditionalgraph learning tasks, such as node classification and link detection, thenetworked data under the causal inference problem has its particularity, i.e.,imbalanced network structure. In this paper, we propose a Graph InfomaxAdversarial Learning (GIAL) model for treatment effect estimation, which makesfull use of the network structure to capture more information by recognizingthe imbalance in network structure. We evaluate the performance of our GIALmodel on two benchmark datasets, and the results demonstrate superiority overthe state-of-the-art methods.

Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data