This course presents a general framework for causal inference:

1. clear statement of the scientific question,

2. definition of the causal model and parameter of interest,

3. 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 semi-parametric methods,

5. interpretation of findings.

 

The estimation methods include G-computation, inverse probability of weighting (IPW), and targeted maximum likelihood estimation (TMLE) with Super Learner.

 

• Developed with Dr. Maya Petersen of UC Berkeley

• Winner of 2014 ASA’s Causality in Statistics Education Award - “individual or team that does the most to enhance the teaching and learning of causal inference in introductory statistics courses”

• Course materials available at www.ucbbiostat.com