Nima Hejazi UC Berkeley
I am a PhD candidate in Biostatistics, working jointly with Mark van der Laan and Alan Hubbard. I am a founding core developer of the tlverse project, the software ecosystem for Targeted Learning. My research interests sit primarily at the intersection of causal inference and machine learning, with a particular concern towards developing efficient and robust statistical procedures for evaluating complex target estimands within observational studies and randomized trials. Broadly, my work draws on ideas from non/semi-parametric estimation in large, flexible statistical models; high-dimensional inference; targeted loss-based estimation; statistical computing; computational biology; and statistical epidemiology. Of late, my methodological work has touched on causal mediation analysis, stochastic treatment regimes, robust inference in two-phase designs, and efficient estimation with sieve-type methods.