Jasjeet Sekhon

Jasjeet "Jas" Singh Sekhon is a data scientist, political scientist, and statistician at Yale University. Sekhon is the Eugene Meyer Professor at Yale University, a fellow of the American Statistical Association, and a fellow of the Society for Political Methodology. Sekhon's primary research interests lie in causal inference, machine learning, and their intersection. He has also published research on their application in various fields including voting behavior, online experimentation, epidemiology, and medicine.

Biography Sekhon graduated with a B.A. from the University of British Columbia. In 1999, he earned a Ph.D. at Cornell University.

Sekhon's career in academia began in 1999, when he became an assistant professor at Harvard University. He stayed at Harvard until 2005 when he moved to UC Berkeley. At Berkeley, he was appointed as the Robson Professor of Political Science and Statistics in 2014. In 2018, he accepted a non-academic position at Bridgewater Associates, where he was chief scientist and head of AI/ML. In 2020, he left Berkeley to join Yale University, where he was appointed Meyer Professor of Political Science and Statistics and Data Science in 2021. He was named a fellow of the Society for Political Methodology in 2019

Sekhon has authored or co-authored dozens of journal articles and several widely used software packages. The topics of his scholarship include experimental research methods, machine learning for estimating causal effects, election fraud, and matching. His research has been widely cited.

Research Sekhon is best known for his research in causal inference and machine learning. His early research on causal inference focused on the role of matching, but he later wrote an article pointing out that matching is unable to address many of the problems (particularly the selection on observables assumption) that its proponents assume. Nevertheless, his Genetic Matching algorithm remains one of his most highly cited articles.

Selected publications