Jens Hainmueller
Kimberly Glenn Professor at Stanford University

Jens Hainmueller is the Kimberly Glenn Professor of Political Science and Director of Graduate Studies in Stanford’s Department of Political Science. He co-directs the Stanford Immigration Policy Lab (IPL) and is a Faculty Affiliate at the Stanford Center for Causal Science, the Institute for Human-Centered Artificial Intelligence, and the Europe Center. He is also a member of the Maternal Child Health Research Institute at Stanford’s School of Medicine.
Hainmueller’s research spans statistical methods, causal inference, immigration, and political economy, and he has published 70 articles with over 40,000 citations. Many of his works appear in top journals, including Science, Nature, and PNAS, as well as leading field journals in political science, statistics, economics, and business.
He has developed widely adopted statistical methods—such as synthetic control methods, entropy balancing, average marginal component effects, and GeoMatch algorithms—and created several open-source software packages that support empirical research across disciplines. At Stanford, he teaches course on causal inference and data science.
Hainmueller’s contributions have been recognized with various awards, including the Gosnell Prize for Excellence in Political Methodology, the Warren Miller Prize, the Robert H. Durr Award, and the Emerging Scholar Award from the Society of Political Methodology. He is an Andrew Carnegie Fellow, an elected Fellow of the Society of Political Methodology, and holds an honorary degree from the European University Institute (EUI).
He completed his PhD at Harvard University, with additional studies at the London School of Economics, Brown University, and the University of Tuebingen. Before joining Stanford, he was a faculty member at the Massachusetts Institute of Technology (MIT).
selected publications
- Political AnalysisCombining outcome-based and preference-based matching: A constrained priority mechanismPolitical Analysis, 2022
- Political AnalysisCausal inference in conjoint analysis: Understanding multidimensional choices via stated preference experimentsPolitical Analysis, 2014
- Political AnalysisEntropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studiesPolitical Analysis, 2012
- JASASynthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control programJournal of the American Statistical Association, 2010