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Causal Inference 2

Columbia University via Coursera

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Overview

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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 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships.

We will study advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models.

Syllabus

  1. Module 7: Introduction to Mediation
  2. Module 8: More on Mediation
  3. Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
  4. Module 10: Longitudinal Causal Inference
  5. Module 11: Interference and Fixed Effects

 

Taught by

Michael E. Sobel

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