3.2 — DAGs — R Practice

Required Packages

Load all the required packages we will use by running (clicking the green play button) the chunk below:

library(tidyverse) # your friend and mine
library(dagitty) # for working with DAGs
library(ggdag) # for drawing DAGs in R

set.seed(20) # using this number means all "random" generated objects will be identical for all of us!

For each of the following examples:

1. Write out all of the causal pathways from X (treatment of interest) to Y (outcome of interest).
2. Identify which variable(s) need to be controlled to estimate the causal effect of X on Y. You can use dagitty.net to help you, but you should start trying to recognize these on your own!
3. Draw the DAGs in r using ggdag. After setting up the dag with dagify() (and specifying exposure and outcome inside dagify), pipe that into ggdag(). Try again piping it instead into ggdag_status() (to highlight what is X and what is Y). Try again piping it instead into ggdag_adjustment_set() to show what needs to be controlled.

Don’t forget to install ggdag and dagitty!

Question 1

Part I

Pathways:

1. \(X \rightarrow Y\) (causal, front door)
2. \(X \leftarrow Z \rightarrow Y\) (not causal, back door)