- Sharad Goel, PhD — Professor of Public Policy, Harvard Kennedy School
423 Guardian Drive
Philadelphia, PA 19104
Included-variable bias and everything but the kitchen sink.
Attendees may attend virtually. Zoom link here.
When estimating the risk of an adverse outcome, common statistical guidance is to include all available factors to maximize predictive performance. Similarly, in observational studies of discrimination, general practice is to adjust for all potential confounds to isolate any impermissible effect of legally protected traits, like race or gender, on decisions. I’ll argue that this popular “kitchen-sink” approach can in fact worsen predictions in the first case and yield conservative estimates of discrimination in the second.
Sharad Goel is a Professor of Public Policy at Harvard Kennedy School. He looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social and political issues, including criminal justice reform, democratic governance, and the equitable design of algorithms. Prior to joining Harvard, Sharad was on the faculty at Stanford University, with appointments in management science & engineering, computer science, sociology, and the law school. He holds an undergraduate degree in mathematics from the University of Chicago, as well as a master’s degree in computer science and a doctorate in applied mathematics from Cornell University.