Lauren Forrow is a senior statistician specializing in causal inference, with expertise in comparison group selection and Bayesian hierarchical modeling. Forrow has been involved in methods development in both areas, helping to develop new matching and weighting techniques and extending cutting-edge Bayesian approaches to causal inference.
Forrow has led the tasks to select comparison groups for several large high-profile health care demonstrations, including Comprehensive Primary Care Plus, the Health Care Innovation Awards Round 2, the Transforming Clinical Practice Initiative, and most recently the Primary Care First evaluation. She also led the Bayesian impact analysis of the Health Care Innovation Awards Round 2 and the Medicaid study for the Transforming Clinical Practice Initiative.
Forrow is a member of the American Statistical Society and its service arm, Statisticians Without Borders. She holds a master’s degree in statistics from Harvard University.
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