Rui Song: On causal decision making

Presentation slides available on SLDS Google Drive:

American Statistical Association (ASA), Section on Statistical Learning and Data Science (SLDS)
February webinar: On causal decision making

Record: March 14, 2023

Presenter: Rui Song is a senior principal scientist at Amazon. She got her PhD in Statistics from University of Wisconsin in 2006 and has been a faculty member at North Carolina State University since 2012. Her research interests include reinforcement learning, causal inference, precision health and knowledge graph. Her research has been supported as principal investigator by National Science Foundation (NSF) including the NSF Faculty Early Career Development (CAREER) Award. She has served as an associate editor for several statistical journals. She is an elected Fellow of the American Statistical Association and Institute of Mathematical Statistics.

Abstract: Decision making is a fundamental aspect of human behavior. Effective decision making can lead to better outcomes, which requires an in-depth understanding of the causal relations between actions, environments, and outcomes. For example, a doctor needs to have a good understanding of the treatment effect in order to develop an effective treatment regime for the patients. In this talk, we will surface three essential components of decision making through a causal lens: 1. identifying causal relationships through causal structural learning; 2. understanding the effects of these relationships through causal effect learning, and 3. applying the above knowledge to inform decision-making through causal policy learning. Key ideas and methods will be discussed, accompanied by real-world examples to illustrate the application.

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[johndziak]

Thank you for this very helpful overview!