Introduction to Causal Inference for Public Health Professionals
Sorry! The enrollment period is currently closed. Please check back soon.
Full course description
With the ongoing “data explosion,” methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce a general framework for Causal Inference in Public Health: 1) clear statement of the research question, 2) definition of the causal model and effect of interest, 3) an assessment of identifiability - that is, linking the causal effect to a parameter estimable from the observed data distribution, 4) choice and implementation of estimators including parametric and non-parametric methods, and 5) appropriate interpretation of findings. The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner, an ensemble machine learning method. The emphasis will be on practical implementation and real-world challenges and solutions.
You will gain experience implementing these estimators and interpreting results through case studies, R labs, R assignments, and a final project using real data. By the end of the course, you will have the practical tools to assess cause-and-effect in your applied work.