In the third part of this series, I will introduce the concepts of causal mediation analysis and the identification strategies for the controlled direct effect, natural direct effect, and natural indirect effect.
In the second part of this series, I will emphasize the graphical understanding of the causal models, and introduce the instrumental variables methods to estimate local (or complier) causal effects, which can be used to address unmeasured confounders.
In the first part of the series, I will cover the basics of causal inference by defining causal estimands using potential outcomes framework, and introduce the methods of matching and inverse probability of treatment weighting to address measured confounders.