3. The Mathematical framework for causal mediation analysis
In this section, we start by reviewing the ingredients of causal mediation analysis including definition of causal estimands/effects and the identification assumptions needed to learn those effects from observed data. We then describe how those causal estimands can be estimated from observed data using the Bayesian g-formula approach. To simplify our presentation, we restrict our examples to the context of an observed set of time-fixed variables.