Brian G. Vegetabile

Photo of Brian Vegetabile
Statistician
Santa Monica Office

Education

Ph.D. in statistics, UC Irvine; M.S. in statistics, UC Irvine; B.S. in aerospace engineering, Penn State University

Overview

Brian Vegetabile is a statistician at the RAND Corporation. His current methodological research interests are in Bayesian inference and statistical machine learning techniques as they apply to causal inference in observational studies. His prior research was motivated by problems arising from the Conte Center on Brain Programming and Adolescent Vulnerabilities at the University of California, where he also studied methods for quantifying predictability in mother-infant interactions as a marker for later behavioral outcomes in observational data settings. Vegetabile earned a B.S. in aerospace engineering from Penn State University and spent four years working as a satellite systems engineer for Northrop Grumman. His Ph.D. thesis focused on methods for obtaining optimal covariate balance for causal inference in observational studies. He obtained his Ph.D. in statistics from the University of California, Irvine.

Concurrent Non-RAND Positions

Associate Editor, Annals of Applied Staitstics

Previous Positions

Satellite Systems Engineer, Northrop Grumman; Systems Engineer, Northrop Grumman

Selected Publications

Brian G. Vegetabile, Beth Ann Griffin, Donna L. Coffman, Matthew Cefalu, Michael W. Robbins & Daniel F. McCaffrey, "Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures," Health Services and Outcomes Research Methodology, 21, 2021

Terry L Schell, Samuel Peterson, Brian G. Vegetabile, Adam Scherling, Rosanna Smart, Andrew R Morral, State-level estimates of household firearm ownership, RAND Corporation (TL-354), 2020

Beth Ann Griffin, Lynsay Ayer, Joseph Pane, Brian Vegetabile, Lane Burgette, Daniel McCaffrey, Donna L Coffman, Matthew Cefalu, Rod Funk, Mark D Godley, "Expanding outcomes when considering the relative effectiveness of two evidence-based outpatient treatment programs for adolescents," Journal of Substance Abuse Treatment, 118, 2020

Brian G. Vegetabile, Daniel L. Gillen, Hal S. Stern, "Optimally balanced Gaussian process propensity scores for estimating treatment effects," Journal of the Royal Statistical Society, Series A - Statistics in Society, 183, 2020

Brian G Vegetabile, Stephanie A Stout-Oswald, Elysia Poggi Davis, Tallie Z Baram, Hal S Stern, "Estimating the entropy rate of finite Markov Chains with application to behavior studies," Journal of Educational and Behavioral Statistics, 44(3), 2019

Honors & Awards

  • UAI Top Reviewer, Uncertainty in Artificial Intelligence
  • ICML Top Reviewer, International Conference on Machine Learning

Publications