The potential outcomes framework often uses one or more parametric outcome models to learn about underlying causal processes. In Stan, parameter estimation using observed data takes place in the model block, while simulation-based estimation of causal parameters using the g-formula can be done separately with generated quantities. Bayesian estimation allows for data-driven sensitivity analysis regarding the assumption of no unmeasured confounding. This presentation shows some simple causal models, then outlines a basic sensitivity analysis using prior information derived from an external data source.

Code and data available at github.com/stan-dev/stancon_talks