My research interests span 

• causal inference,
• high-dimensional data,
• machine learning, and
• non- and semi-parametric theory.

I am interested in applications in

• criminal justice,
• health services,
• medicine, and
• public policy. ​

Biography

Edward Kennedy is an assistant professor of Statistics & Data Science at Carnegie Mellon University. He joined the department after graduating with a PhD in biostatistics from the University of Pennsylvania. Prior to that he received an MA in statistics from the Wharton School, an MS in biostatistics from the University of Michigan, and a BA in mathematics from the University of Pennsylvania. Edward's methodological interests lie at the intersection of causal inference, machine learning, and nonparametric theory, especially in settings involving high-dimensional and otherwise complex data. His applied research focuses on problems in criminal justice, health services, medicine, and public policy. Edward is a recipient of the NSF CAREER award, the David P. Byar Young Investigator award, and the Thomas Ten Have Award for exceptional research in causal inference.