Visit the main workshop repository
This repo is a snapshot in time of the workshop delivered at rstudio::conf(2022). Visit the workshop’s main repo for the latest version of the material.
Causal Inference in R Workshop
🗓️ July 25 and 26, 2022
⏰ 09:00 - 17:00
🏨 National Harbor 3
✍️ rstd.io/conf
Slides
- 00 Intro
- 01 Whole Game
- 02 When Standard Methods Succeed
- 03 Causal Inference with
group_byandsummarise - 04 Causal Diagrams
- 05 Introduction to Propensity Scores
- 06 Using Propensity Scores
- 07 Checking Propensity Scores
- 08 Fitting the outcome model
- 09 Continuous Exposures
- 10 G-Computation
- 11 Tipping Point Sensitivity Analyses
- 12 Whole Game (Your Turn)
Installing materials locally
We will be using RStudio Cloud for the workshop, but if you would like to install the required packages and course materials, we have an R package called {causalworkshop} to help you do that! You can install {causalworkshop} from GitHub with:
install.packages("remotes")
remotes::install_github("malcolmbarrett/causalworkshop")Once you’ve installed the package, install the workshop with
causalworkshop::install_workshop()By default, this package downloads the materials to a conspicuous place
like your Desktop. You can also tell install_workshop() exactly where
to put the materials:
causalworkshop::install_workshop("a/path/on/your/computer")Schedule
Day 1
| Time | Activity |
|---|---|
| 09:00 - 10:30 | Session 1 |
| 10:30 - 11:00 | Coffee break |
| 11:00 - 12:30 | Session 2 |
| 12:30 - 13:30 | Lunch break |
| 13:30 - 15:00 | Session 3 |
| 15:00 - 15:30 | Coffee break |
| 15:30 - 17:00 | Session 4 |
Day 2
| Time | Activity |
|---|---|
| 09:00 - 10:30 | Session 1 |
| 10:30 - 11:00 | Coffee break |
| 11:00 - 12:30 | Session 2 |
| 12:30 - 13:30 | Lunch break |
| 13:30 - 15:00 | Session 3 |
| 15:00 - 15:30 | Coffee break |
| 15:30 - 17:00 | Session 4 |
Instructor
Lucy D’Agostino McGowan is an assistant professor in the Mathematics and Statistics Department at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on statistical communication, causal inference, data science pedagogy, and human-data interaction. Dr. D’Agostino McGowan is the past chair of the American Statistical Association’s Committee on Women in Statistics, chair elect for the Section on Statistical Graphics, and can be found blogging at livefreeordichotomize.com, on Twitter @LucyStats, and podcasting on the American Journal of Epidemiology partner podcast, Casual Inference.
Malcolm Barrett is a data scientist and an epidemiologist. During his Ph.D., he studied vision loss, focusing on epidemiologic methods. He’s since worked in the private sector, including Teladoc Health and Apple. Malcolm is also the author of several causal inference-focused R packages, such as ggdag and tidysmd. He regularly contributes to other open source software, including favorite community projects like usethis, ggplot2, R Markdown.
