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.