Causal inference, graphical models, machine learning in public policy and biomedicine
Daniel is a postdoctoral fellow working with Ilya Shpitser (JHU) and Eric Tchetgen Tchetgen (UPenn) on causal inference with graphical models. His primary interest has been causal structure learning, i.e., data-driven methods for selecting causal graphical models from observational data. Currently, he is thinking about missing data, semiparametric inference, and stochastic processes. Daniel has a BA from Columbia University and defended his PhD at Carnegie Mellon University in December 2017.