This workshop will introduce participants to the “causal roadmap” approach to epidemiologic questions, with a focus on parameter estimation, including: 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, and 5) interpretation of findings. Participants will work through the roadmap using an applied example of early childhood adversity and psychopathology. Using simulations, substitution estimators (e.g., parametric g-formula, targeted minimum loss-based estimator) and estimating equation estimators (e.g., inverse weighting methods) will be implemented by participants in R during the workshop session.
