Jennifer Starling

Jennifer Starling is a senior statistician specializing in Bayesian hierarchical modeling, causal inference, and real-world evidence generation, with expertise in quasi-experimental design, subgroup analysis, and statistical communication. Dr. Starling has contributed to the development and application of innovative Bayesian and machine learning methods for policy evaluation, health care quality measurement, and education research, including methods for heterogeneous treatment effect estimation, performance measure stabilization, and longitudinal data analysis.

Dr. Starling has led quantitative analyses for numerous high-profile federal and state initiatives, including the evaluation of the Primary Care First model, the California Youth Behavioral Health Initiative, the Vermont Payer Equity Analyses, and multiple Centers for Medicare & Medicaid Services quality reporting and demonstration projects. She has developed Bayesian approaches for identifying top-performing health care practices, improving the reliability of school accountability measures, and strengthening subgroup analyses in large-scale program evaluations.

In addition to her applied policy work, Dr. Starling has contributed to methodological research in Bayesian causal forests, varying coefficient models, and targeted smoothing approaches for hierarchical and longitudinal data. Her work has appeared in Bayesian Analysis, the Annals of Applied Statistics, JAMA, and other peer-reviewed journals. She has presented her research at the Joint Statistical Meetings, ISBA, and the Federal Committee on Statistical Methodology Research Conference, among other venues. She received the Thomas R. Ten Have Award from the Atlantic Causal Inference Conference for her work in heterogeneous treatment effect estimation. Dr. Starling holds a Ph.D. in statistics from The University of Texas at Austin.