Laura develops statistical methods for learning about health policy in the real world. As a senior fellow in NORC’s Statistics and Data Science department, she combines modern causal inference and large-scale health data to answer practical questions about health care quality, cost, and outcomes.

Her methodological research centers on two challenges. First, she develops methods for evaluating programs and policies when randomized experiments are not feasible. She has helped shape modern difference-in-differences approaches for observational data and applied them extensively to Medicare and Medicaid policy evaluations. Second, she develops methods that reveal patterns of variation in health care systems. Her work has been applied to settings ranging from individual cancer treatment trajectories to international comparisons of hospital care.

Laura leads and collaborates on research in cancer care, cardiovascular disease, alternative payment models, and comparative health system performance. She currently leads the Methods and Theory Core of an NIA-funded program project on Medicare and the Methods Core of an NCI-funded program project on oncology care. She also advises NORC’s Evaluation of the ACO REACH Model. She serves as statistical editor for JAMA and previously spent 13 years on the faculty of Harvard Medical School.