Video Highlights: Machine Learning and Causal Inference

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For our current edition of “Video Highlights” I’d like to offer this talk that will review 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 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.

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