Edward Kennedy

Edward H. Kennedy is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. He joined CMU in 2016 as an Assistant Professor after earning his PhD in Biostatistics from the University of Pennsylvania, alongside 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.

His research focuses on causal inference, missing data problems, nonparametric methods, and machine learning, with applications in criminal justice, health services, medicine, and public policy. Key areas include incremental treatment effects, counterfactual analysis, and semiparametric sensitivity frameworks. He has developed methodologies for causal clustering, functional average treatment effects, and robust population size estimation.

Edward's work bridges theoretical statistics and practical applications, addressing challenges in high-dimensional and complex data. His contributions span methodological advancements in instrumental variables, doubly robust estimation, and counterfactual fairness in algorithmic decision-making. His research also extends to policy-relevant domains such as healthcare resource allocation, criminal justice reform, and pandemic response analysis.

Edward is actively involved in interdisciplinary collaborations, mentoring students in statistical theory and applied data science. His lab emphasizes rigorous methodological innovation while addressing real-world societal challenges through data-driven approaches.