https://github.com/nhejazi/ser2024_mediation_workshop
Materials for the workshop "Modern Causal Mediation Analysis" at the 2024 Society for Epidemiologic Research (SER) annual meeting in Austin, TX
https://github.com/nhejazi/ser2024_mediation_workshop
Last synced: 5 months ago
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Materials for the workshop "Modern Causal Mediation Analysis" at the 2024 Society for Epidemiologic Research (SER) annual meeting in Austin, TX
- Host: GitHub
- URL: https://github.com/nhejazi/ser2024_mediation_workshop
- Owner: nhejazi
- License: gpl-3.0
- Created: 2024-06-15T18:39:58.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-06-18T20:06:03.000Z (11 months ago)
- Last Synced: 2024-06-19T18:15:51.711Z (11 months ago)
- Language: JavaScript
- Homepage: https://codex.nimahejazi.org/ser2024_mediation_workshop/
- Size: 2.36 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SER 2024 Modern Causal Mediation Analysis
This is the GitHub repository for the workshop **Modern Causal Mediation
Analysis**, co-taught by [Iván Díaz](https://www.idiaz.xyz/), [Nima
Hejazi](https://nimahejazi.org), and [Kara
Rudolph](https://kararudolph.github.io/) at the [SER 2024 annual
meeting](https://epiresearch.org/annual-meeting/2024-meeting/2024-workshops/).
The workshop materials are built using [Quarto](https://quarto.org) and make
use of the [WebR](https://docs.r-wasm.org/webr/latest/) framework for
interactive execution of `R` code in the browser.
## Course Description
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.