Edward Kennedy

Edward H. Kennedy is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He joined CMU in 2016 after earning his PhD in Biostatistics from the University of Pennsylvania (UPenn). His academic journey includes a BA in Mathematics (UPenn, 2007), MA in Statistics (Wharton School, 2014), MS in Biostatistics (University of Michigan, 2009), and PhD in Biostatistics (UPenn, 2016).

His research focuses on causal inference, machine learning, and nonparametric methods, with applications in criminal justice, healthcare, and public policy. Notable contributions include work on incremental treatment effects, sensitivity analysis, and counterfactual prediction. Key awards include the Mortimer Spiegelman Award, NSF CAREER Award, and Thomas Ten Have Award.

Dr. Kennedy advises PhD students on topics ranging from theoretical statistics to applied policy analysis. His work integrates advanced statistical techniques with real-world challenges, emphasizing methodological rigor and practical impact. He maintains an active research group and contributes to open-source software like the AIPW and drpop packages.