This is the GitHub repository for the workshop Modern Causal Mediation
Analysis, co-taught by Iván Díaz, Nima
Hejazi, and Kara
Rudolph at the SER 2024 annual
meeting.
The workshop materials are built using Quarto and make
use of the WebR framework for
interactive execution of R code in the browser.
Causal mediation analysis can provide a mechanistic understanding of how an
exposure impacts an outcome, a central goal in epidemiology and health and
social sciences. However, rapid methodologic developments coupled with few
formal courses presents challenges to implementation. Beginning with an overview
of classical direct and indirect effects, this workshop will present recent
advances that overcome limitations of previous methods, allowing for: (i)
continuous exposures, (ii) multiple, non-independent mediators, and (iii)
effects identifiable in the presence of intermediate confounders affected by
exposure. Emphasis will be placed on flexible, stochastic and interventional
direct and indirect effects, highlighting how these may be applied to answer
substantive epidemiological questions from real-world studies. Multiply robust,
nonparametric estimators of these causal effects, and free and open source R
packages (medshift and medoutcon) for their application, will be introduced.
To ensure translation to real-world data analysis, this workshop will
incorporate hands-on R programming exercises to allow participants practice in
implementing the statistical tools presented. It is recommended that
participants have working knowledge of the basic notions of causal inference,
including counterfactuals and identification (linking the causal effect to
a parameter estimable from the observed data distribution). Familiarity with the
R programming language is also recommended.