معرفی
Stefan Wager is an Associate Professor at Stanford Graduate School of Business and Courtesy Associate Professor of Statistics. He holds a PhD and BS in Mathematics from Stanford. His research bridges causal inference, machine learning, and decision science, focusing on leveraging modern methods for statistical problems in economics and policy. Wager has advised numerous PhD students and postdocs, and his work has been recognized with awards like the Facebook Faculty Award and the Applied Statistics Dissertation Award. He contributes to open-source tools like the Generalized Random Forests (grf) package and serves on editorial boards for top journals.
Education: PhD in Statistics (Stanford, 2016); BS in Mathematics (Stanford, 2011).
Research Interests: Causal inference, policy learning, treatment effect estimation, machine learning applications in econometrics and healthcare.
Awards: Philip F. Maritz Faculty Scholar (2024–2025), Spence Faculty Scholar, and multiple grants from NSF and industry partners.
Grants: NSF Methodology, Stanford Human-Centered AI, and collaborations with Google, Uber, and Facebook.
His work emphasizes scalable methods for policy evaluation, including synthetic difference-in-differences and optimized regression discontinuity designs. He has developed influential software for causal forests and treatment effect estimation, impacting both academia and industry.
