Alan Hubbard, Professor of Biostatistics at UC Berkeley, works on an estimation of complex causal parameters and prediction algorithms using machine learning, with an emphasis on applications in epidemiology, environmental exposure and biomedicine.
His research focuses on the application of statistics to population studies with particular expertise in semi-parametric models and the use of machine learning in causal inference, as well as applications in high dimensional biology.Read more
Applied work ranges from the molecular biology of aging, wildlife biology, social epidemiology, infectious disease and acute trauma. He is particularly interested in harnessing machine-learning algorithms and advances in semiparametric causal inference towards machines for optimizing the estimation of parameters related to causal inference/variable importance, with particular emphasis on discovering and estimating the impact of treatment rules. In addition, he is currently exploring the application of data-adaptive target parameter approaches to formalize asymptotics for exploratory data analysis, to allow for a lack of a priori specified hypotheses while still providing an estimation of meaningful parameters and estimators with predictable sampling distributions.