Susan Athey: Machine Learning and Causal Inference for Personalization

Guest Speaker: Susan Athey, Economics of Technology Professor, Stanford Graduate School of Business
Hosted by: Mingzhang Yin, Postdoctoral Research Scientist, Data Science Institute
Machine Learning and Causal Inference for Personalization
Abstract: In this talk, Athey will discuss recent work using machine learning tools to estimate optimal treatment assignment policies, as well as tools to evaluate the benefits of such policies. We consider problems of off-policy evaluation in a variety of empirical settings, including problems of firms setting prices. The talk will be moderated by Mingzhang Yin, DSI Postdoctoral Research Scientist.
Bio: Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. Her current research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning. She has worked on several application areas, including timber auctions, internet search, online advertising, the news media, and the application of digital technology to social impact applications. As one of the first “tech economists,” she served as consulting chief economist for Microsoft Corporation for six years, and now serves on the boards of Expedia, Lending Club, Rover, Turo, and Ripple, as well as non-profit Innovations for Poverty Action. She also serves as a long-term advisor to the British Columbia Ministry of Forests, helping architect and implement their auction-based pricing system. She is the founding director of the Golub Capital Social Impact Lab at Stanford GSB, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence.