Academic Lecure on "Causal discovery and applications"

Time: 15:00pm, December 13th, 2018

Place: #60-217

Lecturer: Professor Li Yongjiu, Vice Dean of School of Information Technology and Mathematics, University of South Australia

Content: Various machine learning methods make use of association relationships for classification and decision making. An association shows that two variables exhibit the same (or opposite) trend but may not indicate that the two variables have an inherent relationship. In other words, an association relationship can be spurious and/or conditional. Causal relationship discovery is to find the inherent relationships where the change of one variable leads to the change of another. The identification of casual relationships is crucial for understanding data and supports evidence based decision making. Causal discovery is a central task for science, health, economy and nearly all areas of studies. In this talk, I will discuss our work in the area and applications.