Maria DeYoreo

Photo of Maria DeYoreo
Co-director, RAND Center for Causal Inference; Statistician
Santa Monica Office


postdoc in statistics, Duke University; Ph.D. in statistics, University of California, Santa Cruz; B.S. in mathematical sciences, University of California, Santa Barbara


Maria DeYoreo is a statistician at the RAND Corporation and co-director of the Center for Causal Inference. Her methodological interests include Bayesian/hierarchical modeling, Bayesian nonparametrics, missing data imputation, data fusion, and causal inference. DeYoreo's substantive interests include health care quality and provider performance assessment, military health, mental health and substance abuse treatment and access, and colorectal cancer. She co-leads a team that works with the Centers for Medicare & Medicaid Services to generate the Medicare Advantage Star Ratings (quality and performance ratings for health plans). Her research experience also includes the following topic areas: microsimulation models for colorectal cancer, sexual assault and suicide in the military, health care consolidation/health systems, and maternal/child health. DeYoreo received her B.S. in mathematical sciences from the University of California, Santa Barbara, and her Ph.D. in statistics from the University of California, Santa Cruz, where she developed flexible Bayesian nonparametric models for ordinal regression. She completed postdoctoral research at Duke University, where she developed imputation models for mixed multivariate outcomes, and modeling approaches for data fusion and measurement error.

Recent Projects

  • Analysis Related to Medicare Advantage and Part D Contract Star Ratings
  • Understanding the Relationship Between Health Care Delivery Systems and PCOR Processes and Outcomes
  • Comparative Modeling of Colorectal Cancer: Informing Health Policies and Prioritizing Future Research
  • Development of a Survey to Assess Care Experiences of the Seriously Ill

Honors & Awards

  • The Savage Award, International Society for Bayesian Analysis