My research centers on methodology for causal inference in observational studies. I develop new ways to form matched comparison groups in large observational datasets using approaches from discrete optimization. These tools allow transparent and interpretable inferences about the effects of interventions, and provide opportunities to study the impact of potential unobserved confounding variables. I am also interested in applying these methods in health services research, public policy, and the social sciences.
Research Expertise and Interest
causal inference, health services & policy analysis, biostatistics, discrete optimization