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Jens Hainmueller
Kimberly Glenn Professor and Professor of Political Science
Ph.D., Harvard University, Government (2009)
M.P.A., Harvard University, Kennedy School of Government, Master of Public Administration (2005)
M.Sc., London School of Economics, International Political Economy (2003)
B.A. (Zwischenprüfung), Tubingen University, Major: Political Science; Minors: Economics and Public Law (2001)
Jens Hainmueller is the Kimberly Glenn Professor of Political Science and Director of Graduate Studies in Stanford University’s Department of Political Science. He co-directs the Stanford Immigration Policy Lab 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 nearly 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 courses on causal inference and data science.
Hainmueller’s contributions have earned him prestigious 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 earned his PhD from 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).
For a full list of his publications, please refer to his Google Scholar Citation Page and CV.
Hainmueller’s research spans statistical methods, causal inference, immigration, and political economy, and he has published nearly 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 courses on causal inference and data science.
Hainmueller’s contributions have earned him prestigious 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 earned his PhD from 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).
For a full list of his publications, please refer to his Google Scholar Citation Page and CV.