I am a postdoctoral research fellow in the Program on Causal Inference at the Harvard School of Public Health. Beginning in August, 2013, I will be an assistant professor in the Department of Biostatistics at Johns Hopkins University.
My research is in causal inference and epidemiologic methods. Broadly, I am interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met. I have worked on characterizing the bias that results from misclassification, i.e. violations of the assumption that variables were measured accurately. Much of my dissertation focused on semiparametric estimation of instrumental variables models, as these models are useful for certain violations of “no unmeasured confounding” assumptions. I am currently working on developing new methods for statistical and causal inference in the presence of interference (when one subject’s treatment may affect other subjects’ outcomes) and for social network data; both of these represent violations of assumptions of independence among observations.
My postdoctoral advisor is Tyler VanderWeele.