Abstract: With the advent of wide access to "big data" machine learning has made enormous advances in supervised and unsupervised techniques. Standard supervised ML focuses on prediction problems, but many real-world policy problems can only be partially addressed using purely predictive techniques. A recent literature has emerged combining techniques from machine learning with tools from the literatures on program evaluation and causal inference. In this talk, I will review recent proposals to solve three distinct but related problems: heterogeneous treatment effect estimation, average treatment effect estimation, and estimation of optimal personalized policies. The methods draw from a variety of literatures in machine learning, and themes include the need to modify standard predictive methods to optimize for causal inference objectives and enable the construction of confidence intervals for parameter estimates, as well as the importance of incorporating insights from the econometrics literature on semi-parametric efficient estimation. I will also highlight the extension of these methods to techniques commonly used to enable causal inference in economic applications, such as instrumental variables.

Short Bio: Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University in economics, computer science, and mathematics, and her Ph.D. from Stanford. She holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. In 2007, Professor Athey received the John Bates Clark Medal, awarded by the American Economic Association to "that American economist under the age of forty who... made the most significant contribution to economic thought and knowledge". She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008, and she is a corresponding fellow of the British Academy. Professor Athey's research focuses on the economics of the internet, online advertising, the news media, marketplace design, and the intersection of computer science, machine learning and economics. She advises governments and businesses on marketplace design and platform economics, including several years as consulting chief economist for Microsoft. She serves on the board of directors of Expedia as well as private companies Rover and Ripple.