Lucy D'Agostino McGowan and Malcom Barret give a tutorial on Causal inference in R. The team covers drawing assumptions on a graph, model assumption, analyzing propensities, and estimating causal effect. Throughout the presentation there are exercises provided as well as a walk through afterwards. This video is part of the virtual useR! 2020 conference.
Find supplementary material on our website https://user2020.r-project.org/.


Main Sections

0:00 Introduction
2:53 Three best practices of analysis
12:03 Causal modeling in R: whole game
24:30 Diagnose your model assumptions
27:18 Estimate the causal effects
30:05 Using {rsample} to bootstrap our causal effect
33:15 Review the R markdown file later!
33:55 Resources
35:15 Causal diagrams in R
37:39 The basic idea
39:17 ggdag
44:09 Exercise 1
50:33 Causal effects and backdoor paths
53:10 Exercise 2
1:01:19 Exercise 3
1:07:19 Resources: ggdag vignettes
1:08:43 Propensity Scores
1:15:52 Exercise 1
1:20:10 Walk through
1:25:28 Propensity scores weighting
1:36:55 Exercise 2
1:40:35 Walkthrough
1:42:32 Propensity score diagnostic
1:44:43 SMD in R
1:53:43 Outcome model
1:57:09 Exercise
2:08:58 Walkthrough
2:11:30 Thank you!


More Resources

Main Site: https://www.r-consortium.org/
News: https://www.r-consortium.org/news
Blog: https://www.r-consortium.org/news/blog
Join: https://www.r-consortium.org/about/join
Twitter:   / rconsortium  
LinkedIn:   / r-consortium