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Stefan Wager
Stefan Wager
Assistant Professor, Operations, Information & Technology
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+1 (650) 721-6152
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StefanWager
Assistant Professor of Operations, Information, and Technology
Assistant Professor of Statistics (by courtesy), School of Humanities and Sciences
Spence Faculty Scholar for 2019-2020
Academic Area: 
Operations, Information & Technology

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

Publications

Journal Articles

Debiased Inference of Average Partial Effects in Single-Index Models: Comment on Wooldridge and Zhuv
Stefan Wager, David A. Hirshberg. Journal of Business & Economic Statistics. December 20, 2019, Vol. 38, Issue 1, Pages 19-24.
Optimized Regression Discontinuity Designs
Guido W. Imbens, Stefan Wager. Review of Economics & Statistics. May 2019, Vol. 101, Issue 2, Pages 264-278.
Generalized Random Forests
Susan Athey, Stefan Wager, Julie Tibshirani. Annals of Statistics. 2019, Vol. 47, Issue 2, Pages 1148-1178.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager, Susan Athey. Journal of the American Statistical Association. June 6, 2018.
Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
Susan Athey, Guido W. Imbens, Stefan Wager. Journal of the Royal Statistical Society-Series B. February 18, 2018, Vol. 80, Issue 4, Pages 597-623.

Working Papers

Sufficient Representations for Categorical Variables | PDF
Jonathan Johannemann, Vitor Hadad, Susan Athey, Stefan Wager, August 2019
Estimating Treatment Effects with Causal Forests: An Application | PDF
Susan Athey, Stefan Wager, February 202019
Synthetic Difference in Differences | PDF
Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager, February 12019
Offline Multi-Action Policy Learning: Generalization and Optimization | PDF
Susan Athey, Zhengyuan Zhou, Stefan Wager, October 102018
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | PDF
Susan Athey, Stefan Wager, July 102017
Adaptive Concentration of Regression Trees, with Application to Random Forests
Stefan Wager, Guenther Walther, April 302016

Teaching

Degree Courses

2019-20

MGTECON 634: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual...

OIT 276: Data and Decisions - Accelerated (Flipped Classroom)

Accelerated Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a more lab-based classroom approach. Traditional...

OIT 644: Research in Operations, Information and Technology

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research...

2018-19

MGTECON 634: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual...

OIT 274: Data and Decisions - Base (Flipped Classroom)

Base Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a lab-based classroom approach. Traditional lecture...

OIT 644: Research in Operations, Information and Technology

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research...

Stanford University Affiliations

Greater Stanford University

  • Assistant Professor of Statistics (by courtesy), School of Humanities and Sciences, 2017-present

Insights by Stanford Business

writtenSusan Athey: Why Business Leaders Shouldn’t Have Blind Faith in AI
May 23, 2018
Telling cats from dogs is easy. It’s the what-ifs that get problematic.

School News

writtenFrom the Dean: Staying Ahead in the Digital Age
February 27, 2018
“Effective leaders in the digital age will need to be mindful of the social consequences of technology.”
writtenNew Faculty and Electives Enrich Student Experience
September 19, 2017
The return of the school year welcomes new tenure-line faculty and lecturers, plus new electives to broaden the student experience.