Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
This work addresses the problem of causal inference with hidden confounders for researchers in domains like healthcare or social sciences, representing an incremental improvement by adapting graph learning to networked observational data.
The paper tackles treatment effect estimation from networked observational data by addressing hidden confounders through a Graph Infomax Adversarial Learning model that leverages imbalanced network structures, achieving superior performance over state-of-the-art methods on two benchmark datasets.
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.