Christopher Walters: Empirical Bayes and large-scale inference

"Empirical Bayes and large-scale inference"
Christopher Walters
University of California Berkeley
Wednesday, May 15, 2024

Biography:
Christopher Walters is an Associate Professor of Economics in the Department of Economics at the University of California, Berkeley. Dr. Walters joined the faculty at Berkeley after completing his PhD in economics at MIT in 2013. He is also a Research Associate in the NBER programs on education and labor studies, an IZA Research Fellow and an affiliate of JPAL-North America and MIT’s Blueprint Labs. His academic research focuses on topics in labor economics, the economics of education, and applied econometrics, including work on school choice, early childhood programs, methods for evaluating school quality, experimental measurement of labor market discrimination, causal inference, and empirical Bayes methods.

Abstract:
This workshop will cover empirical Bayes methods for studying heterogeneity, estimating individual effects, and making decisions in settings with many unit-specific parameters. Examples include studies of school, teacher, and physician quality; neighborhood effects on economic mobility; firm effects on wages; employer-specific labor market discrimination; and individualized treatment effect predictions and policy recommendations. Topics will include methods for quantifying variation in effects, empirical Bayes shrinkage, connections to machine learning methods, and large-scale inference tools for multiple testing and decision-making. The lecture will be accompanied by coding examples.