Michael E. Sobel

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.

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Causal Inference 2 (Coursera)

This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last [...]

Causal Inference (Coursera)

This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and [...]