AAAI-23 Tutorial Introduction Team Schedule Materials


Download our tutorial slides!


        Causal inference has numerous real-world applications in many domains such as education, health care, political science, marketing, and online advertising. Causal inference has been studied for decades, however, traditional methods may have limited capability on handling large-scale and high-dimensional heterogeneous data. Recent research efforts demonstrate that machine learning could greatly facilitate causal inference tasks such as treatment effect estimation and counterfactual inference. Meanwhile, casual knowledge extracted from observational data could enhance the reliability of machine learning models. In this tutorial, we will present a comprehensive overview of the intersection of causal inference and machine learning. We will start with the background of causal inference and briefly introduce several traditional causal inference methods. Then we will introduce the state-of-the-art machine learning algorithms for causal inference, especially the representation learning based methods and graph neural networks based methods. In addition, we will discuss how to exploit causal knowledge for trustworthy machine learning, such as explanation, fairness, robustness, and so on. Finally, we showcase promising industrial applications of these methods in multiple domains.



Meet our team


Zhixuan Chu


Ant Group


Jing Ma

Assistant Professor

Case Western Reserve University


Jundong Li

Assistant Professor

University of Virginia


Sheng Li

Assistant Professor

University of Virginia



Date: February 7, 2023

Location: Washington, DC, USA

Address: Walter E. Washington Convention Center, Room 202A


    1.Welcome from Organizers

    2.Background on Causal Inference

    3.Representation Learning based Methods

    4.Graph Neural Networks based Methods

    5.Causal Inference-aided Machine Learning

    6.Causal Inference Applications

    7.Future Directions




Follow Us

Go To Top