Michael Sobel is a professor of Statistics at Columbia University. His research interests lie primarily in the area of causal inference, where he has published papers on the subjects of mediation, interference, longitudinal causal inference using fixed effects models, meta-analysis, compliance, and causal inference for fMRI experiments, in which massive amounts of time series data are collected for subjects under varying experimental conditions. In addition to extending his work on fMRI, he is also working on interference in observational studies using fixed effects models, and he is working to develop some new estimands for counterfactual inference more broadly.
Coursera
Dive into a Master’s-level exploration of causal inference with this rigorous mathematical survey course on Coursera. Gain essential insights into the statistical methods used to make informed decisions about causation across various fields such as science, medicine, policy, and business.
Coursera
Dive into the world of Causal Inference 2, a Coursera course designed to equip you with advanced skills in understanding and analyzing causal relationships using sophisticated statistical methods. This course is perfect for those looking to deepen their knowledge in areas critical to scientific research, healthcare, policy-making, and business strategy.