This document provides an overview of DAGitty, a web-based software for drawing and analyzing causal diagrams or directed acyclic graphs (DAGs). DAGitty allows users to create causal diagrams by adding nodes and connecting them with arrows. It identifies adjustment sets needed to estimate causal effects and detects biasing paths. The software provides features for changing node attributes, checking implications of the diagram, and saving/loading diagrams. It is a useful tool for visualizing and analyzing the causal relationships in complex systems.

1. Construction of
Causal Diagrams
using DAGitty
Kim Carmela D. Co
Department of Epidemiology and Biostatistics
College of Public Health
University of the Philippines - Manila

2. Introduction
• http://www.dagitty.net/
• Web-based software for drawing and analyzing causal
diagrams, also known as directed acyclic graphs (DAGs)
• Developed by Johannes Textor
• Also available as an R package

3. Utility
• Identification of suitable, small sufficient adjustment sets in
complex causal diagrams
• Identification of causal and biasing paths
• Identification of testable implications in a given diagram
• Identification of potential instrumental variables

6. • Exposure variable is green with a right pointing arrow
• Outcome variable is blue with a bar
• Main association indicated by a green arrow
• Can drag any variable anywhere in the screen

8. Creating the DAG
• To add more variables:
• Double click anywhere in the area or press N (“new”)
• Default color is dark grey
• To place arrows in the causal DAG:
• Double click the variable (or hover cursor on variable and press C
“connect”) where the arrow will come from (“parent variable”)
• Hover cursor on variable where arrow will point to and press C
(“daughter variable”)
• Press C again (while cursor is hovering over the daughter variable)
to remove the arrow

9. • Ancestors of exposure are green, ancestors of outcome are blue
• Ancestors of both exposure and outcome are red
• Red arrows indicate biasing pathways
• Arrows can be left-clicked and dragged to change its shape

10. Changing Variable Status
E •Change variable to an “exposure” of interest
O •Change variable to an “outcome” of interest
R •“Rename” the variable
U •To toggle whether the variable is observed or “unobserved”
A •To toggle whether the variable is “adjusted” or not
D •“Delete” the variable

11. Indicates minimum set of variables that
have to be adjusted for in order to
estimate the causal effect of the
exposure on the outcome
“smallest number of variables”
Minimum set of variables to estimate
the direct effect of the exposure on the
outcome
Removing pathways with intermediate
variables
Identification of potential instrumental
variables, if the model is correctly specified

12. • Independencies implied by the
model
• Can be checked to determine if the
model is correctly specified
• Maternal age and socio-economic
status are independent?

13. Saving the DAG
• Copy model code to notepad
to “save”
• Code can be pasted to
“load” the DAG

14. Open paths = Statistical association
Blocked paths = Statistical independence

16. Exercise
• Open Shrier & Platt, 2008
• Which variables are “confounders” using the traditional
meaning?
• Label team motivation, pregame proprioception,
neuromuscular fatigue, and tissue weakness as unmeasured
variables.
• Adjust for the necessary variables to be able to estimate the
causal effect of interest.
• Adjust for the variable previous injury.

18. Reference
• Textor, Johannes (2015) Drawing and Analyzing Causal
DAGs with DAGitty. Accessed from:
http://www.dagitty.net/manual-2.x.pdf