Jann Spiess

Assistant Professor, Operations, Information & Technology

Jann Spiess

Assistant Professor of Operations, Information & Technology

Center Fellow, Stanford Institute for Economic Policy Research
Assistant Professor of Economics (by courtesy), School of Humanities and Sciences

Research Statement

Jann works on integrating techniques and insights from machine learning into the econometric toolbox. His research brings together microeconometric methods, statistical decision theory, and mechanism design to clarify the use of flexible prediction algorithms in causal inference and data-driven decision-making. He is particularly interested in the role of human and machine decisions in replicable and robust inferences from big data.

Research Interests

  • Data-Driven Decision-Making
  • Machine Learning
  • Econometrics
  • Causal Inference
  • Data Science


Jann holds a PhD in economics from Harvard University. Previously, Jann obtained a master’s degree in public policy from the Harvard Kennedy School. His background is in mathematics with a focus on probability theory and combinatorics, which he studied at the University of Cambridge (Part III of the Mathematical Tripos) and the Technical University of Munich. Jann also studied and worked in Hangzhou, China and Ouagadougou, Burkina Faso.

Academic Degrees

  • PhD, Economics, Harvard University, 2018
  • AM, Economics, Harvard University, 2015
  • MPP, Public Policy, Harvard University, 2013
  • MASt, Mathematics, University of Cambridge, 2011
  • BSc, Mathematics, Technical University of Munich, 2010

Awards and Honors

  • Philip F. Maritz Faculty Scholar for 2021-22
  • David A. Wells Prize for best dissertation, Department of Economics, Harvard University, 2018
  • Restud Tour, 2018


Journal Articles

Lea Bottmer, Guido W. Imbens, Jann Spiess, Merrill Warnick
Journal of Business & Economic Statistics
August 2023
Alberto Abadie, Jann Spiess
Journal of the American Statistical Association
June 2022 Vol. 117 Issue 538 Pages 983–995
K.L. Milkman, L. Gandhi, M.S. Patel, H.N. Graci, D.M. Gromet, H. Ho, J.S. Kay, T.W. Lee, J. Rothschild, J.E. Bogard, I. Brody, C.F. Chabris, E. Chang, G.B. Chapman, Jennifer E. Dannals, N.J. Goldstein, A. Goren, H. Hershfield, A. Hirsch, J. Hmurovic, S. Horn, D.S. Karlan, A.S. Kristal, C. Lamberton, M.N. Meyer, A.H. Oakes, M.E. Schweitzer, M. Shermohammed, J. Talloen, C. Warren, A. Whillans, K.N. Yadav, J.J. Zlatev, R. Berman, C.N. Evans, R. Ladhania, J. Ludwig, N. Mazar, S. Mullainathan, C.K. Snider, Jann Spiess, E. Tsukayama, L. Ungar, C. Van den Bulte, K.G. Volpp, A.L. Duckworth
Proceedings of the National Academy of Sciences of the United States of America
February 8, 2022 Vol. 119 Issue 6
Katherine L. Milkman, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Pepi Pandiloski, Yeji Park, Aneesh Rai, Max Bazerman, John Beshears, Lauri Bonacorsi, Colin Camerer, Edward Chang, Gretchen Chapman, Robert Cialdini, Hengchen Dai, Lauren Eskreis-Winkler, Ayelet Fishbach, James J. Gross, Samantha Horn, Alexa Hubbard, Steven J. Jones, Dean Karlan, Tim Kautz, Erika Kirgios, Joowon Klusowski, Ariella Kristal, Rahul Ladhania, George Loewenstein, Jens Ludwig, Barbara Mellers, Sendhil Mullainathan, Silvia Saccardo, Jann Spiess, Gaurav Suri, Joachim H. Talloen, Jamie Taxer, Yaacov Trope, Lyle Ungar, Kevin G. Volpp, Ashley Whillans, Jonathan Zinman, Angela L. Duckworth
December 2021
Jens Ludwig, Sendhil Mullainathan, Jann Spiess
American Economic Association
May 2019 Vol. 109 Pages 71-76
Jann Spiess, Talia Gillis
The University of Chicago Law Review
March 2019 Vol. 86 Issue 2 Pages 459-487
Jann Spiess, Sendhil Mullainathan
Journal of Economic Perspectives
2017 Vol. 31 Issue 2 Pages 87–106

Stanford GSB Affiliations

Insights by Stanford Business

January 22, 2024
Combining the power of experiments with the potential of machine learning has tremendous implications for designing more effective public policy.
December 12, 2023
’Tis the season for personal and professional growth.
October 25, 2023
Expert advice on how to move fast — without breaking stuff.
October 06, 2022
If a complex data analysis tool can’t explain its decisions, how do we know it’s accurate — or fair?