Biostatistics 690B - Intro to Causal Inference

Big data present exciting opportunities to better understand risk factors, to build improved predictors, and to examine the causal relationships between variables. Still, there are many sources of association between two variables, including direct effects, indirect effects, measured confounding, unmeasured confounding, and selection bias. Methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce 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, and 5) interpretation of findings. The estimation methods include G-computation, inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learning. Students gain practical experience implementing these estimators and interpreting results through discussion assignments, R labs, and R assignments.

BIOSTATS 640 & 650 Requirements: A course in intermediate biostatistics (e.g. BIOSTAT640) and experience with regression modeling (e.g. BIOSTAT650); or instructor permission. Recommended: a course in intermediate epidemiology (e.g. EPI632, EPI737)