Learning Causality with Graphs New Faculty Highlights Extended Abstract

The following article is an extended abstract submitted as part of AAAI's New Faculty Highlights Program. Recent years have witnessed a surge in machine learning methods on graph data, especially those pow- ered by effective neural networks. Despite their success in different real-world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, while they lack any perspective of causality. Causal inference can reveal the causality inside data. An important problem in causal inference is causal effect estimation, which aims to estimate the causal effects of a certain treatment (e.g., prescrip- tion of medicine) on an outcome (e.g., cure of disease).