Name: George Perrett
Email: gp77@nyu.edu
Program: Statistics and Computational Social Science
Research Interests: Causal Inference, Bayesian Statistics, Bayesian Additive Regression Trees, Psychometrics, and the political economy of LLMs
Principal Advisor(s): Klint Kanopka
Research description/bio: George works at the intersection of causal inference and machine learning (using Bayesian methods). Currently he is particularly interested in bridging gaps between causal inference and psychometrics. He began his PhD at NYU in the fall of 2025. He has given talks at the American Causal Inference Conference (ACIC) and will give a talk at the upcoming International Society for Bayesian Analysis (ISBA). Prior to his doctoral studies he was awarded a Masters of Science in Applied Statistics (NYU), a Masters of Public Health (University of Michigan), and a Bachelor of Arts in Psychology (University of Wisconsin).
Selected Awards, Publications, and Presentations:
Perrett, G. Hill, J. Srivastava, A. Scott, M. (2026). Scaffolding responsible software use: evaluating the effectiveness of a causal inference tool. The American Statistician
Dorie, V., Perrett, G., Hill, J. L., & Goodrich, B. (2022). Stan and BART for causal inference: Estimating heterogeneous treatment effects using the power of Stan and the flexibility of machine learning. Entropy, 24(12), 1782.