My research focuses on the development of methods for statistical causal inference in medicine and epidemiology, which can be applied to large-scale and passively collected longitudinal data, e.g., from electronic health records. A primary objective is to expand the set of policy questions that can be explicitly posed within the language of formal causal and statistical frameworks. I view this as a fundamental step in the development of methods aimed to translate data into human good, and one which occurs at the intersection of statistics and the humanities. Currently, my research applies this approach to answer questions about the mechanisms of healthcare interventions and about optimally triaging scarce treatment resources (like ventilators, vaccines, and highly-trained care providers) in complex health care settings, among other topics. My research also applies a critical lens to the dominant paradigms in statistics and causal inference that structure epidemiologic thought and practice.
