Introduction to Parametric and Semi-parametric Estimators for Causal Inference

Introduction to Parametric and Semi-parametric Estimators for Causal Inference

Workshop ChairsJennifer Ahern and Laura Balzer

This workshop will introduce participants to a “causal roadmap” approach to epidemiologic questions: 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. The focus will be on estimation with a simple substitution estimator (parametric g-formula), inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with SuperLearner. Participants will work through the roadmap using an applied example and implement these estimators in R during the workshop session.