3.2 — Causal Inference II: DAGs — R Practice

Set Up

To minimize confusion, I suggest creating a new R Project (e.g. regression_practice) and storing any data in that folder on your computer.

Alternatively, I have made a project in R Studio Cloud that you can use (and not worry about trading room computer limitations), with the data already inside (you will still need to assign it to an object).

• Answer Key (html)
• Answer Key (markdown)

DAG Practice

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 for 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!

Question 1

Question 2

Question 3

Question 4

Question 5