My research interests include methodology for drawing causal inferences from observation data and for handling selection biases and missing data.  A causal inference problem that particularly interests me is time-dependent confounding.  This arises when a time-varying exposure (e.g. treatment) is affected by time-varying confounders that are themselves affected by the exposure.  This confounding complicates the estimation of treatment effects and the identification of optimal treatment regimes for individual patients.  Selection biases of interest include those arising from having an unrepresentative sample or an observation processes that is not independent of the process of interest.  For example, electronic health records may contain more information on sicker patients than on healthier patients.  My missing data interests include foundations of missing data theory, multiple imputation methods, inverse probability and covariate-balancing weights, and double robust methods.  I also have long-standing interests in risk prediction, HIV/AIDS epidemiology and injecting drug use epidemiology.