Introduction to Causal Inference
This course presents a general framework for causal inference:
clear statement of the scientific question,
definition of the causal model and parameter of interest,
assessment of identifiability - that is linking the causal effect to a parameter estimable from the observed data distribution,
choice and implementation of estimators including parametric and semi-parametric methods,
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
Inverse probability weighting
Super Learner (ensemble method)
Targeted maximum likelihood estimation