May 8, 2020
In the IDSS Distinguished Seminar, Professor Susan Athey reviewed a series of recent papers that develop new methods based on Machine Learning methods to approach problems of causal inference, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments, environments with unconfoundedness, instrumental variables, and panel data will be considered.
The distinguished presenter is Susan Athey, The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her Ph.D. from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008. Professor Athey’s research focuses on marketplace design and the intersection of computer science, machine learning and economics.
The original article can be found here.
It is useful to note that AI with machine learning and causal inference has been developed by professor Judea Pearl, who is awarded Turing Award 2011, the most prestigious prize awarded to computer scientists by the Association for Computing Machinery. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea Pearl also contribute on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.