Naimi completed his doctorate in epidemiology at the University of North Carolina at Chapel Hill, and a postdoctoral research fellowship at McGill University. He is a member of the editorial board of Epidemiology, an associate editor of the American Journal of Epidemiology, and is currently a statistical/methods editor of JAMA Open Network and Fertility & Sterility.
His methodological research focuses largely on the development, evaluation, and application of machine learning and causal inference methods to observational and experimental data. His applied research has focused on developing and implementing methods to (1) estimate compliance adjusted effects of daily low-dose aspirin on reproductive outcomes, and (2) model complex synergistic effects of diet on pregnancy outcomes.
He currently teaches intermediate and advanced analytic methods for graduate students at Emory University.
You can visit his personal webpage here.
Design and Analysis of Simulation Studies
This course will focus on how to use experimental principles to appropriately design and analyze Monte Carlo simulation studies.View Details
Machine Learning for Estimating Causal Effects
Through practical data and coding examples, you will learn to use cutting-edge “double-robust” machine learning methods (targeted minimum loss-based estimation, augmented inverse probability weighting) to estimate different treatment effects in real and simulated data.View Details