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 associate 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. Edward's research 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 work focuses on problems in criminal justice, health services, medicine, and public policy. Edward is a recipient of the Mortimer Spiegelman Award for outstanding contributions to health statistics, the NSF CAREER Award, and the Thomas Ten Have Award for exceptional research in causal inference.