Stefan Wager

Research Statement

Professor Wager’s research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to classical problems in statistics, economics and decision making that leverage recent developments in machine learning.

Bio

Stefan Wager is an Assistant Professor of Operations, Information and Technology at Stanford University’s Graduate School of Business, and an Assistant Professor of Statistics (by courtesy). He received his Ph.D. in Statistics from Stanford University in 2016, and also holds a B.Sc. (2011) degree in Mathematics from Stanford. He was a postdoctoral researcher at Columbia University during the academic year 2016-2017, and has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook and Google.

Academic Degrees

• PhD, Statistics, Stanford University, 2016
• BS, Mathematics, Stanford University, 2011

Awards and Honors

• Jerome H. Friedman Applied Statistics Dissertation Award, 2016
• First place finish at the NSF-NGA-DTRA Chemical Detection Algorithms Challenge, 2014
• B. C. and E. J. Eaves Stanford Graduate Fellow, 2011-2014

• J. E. Wallace Sterling Award for Scholastic Achievement, 2011

Teaching

Degree Courses

2017-18

OIT 661: Causal Inference

This course covers mathematical and statistical underpinnings of causal inference, with a focus on scientific experimentation and data-driven decision making. Topics include randomization, potential outcomes, observational studies, double...