Skip to content

Graphical analysis of structural causal models / graphical causal models.

License

Notifications You must be signed in to change notification settings

jtextor/dagitty

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

dagitty

This is a collection of algorithms, a GUI frontend and an R package for analyzing graphical causal models (DAGs).

The main components of the repository are:

  • jslib: a JavaScript library implementing many DAG algorithms. This library underpins both the web interface and the R package, but could also be used independently, like in node.js.
  • gui: HTML interface for a GUI that exposes most of the functions in the JavaScript library.
  • r: R package that exposes most of the functions in the JavaScript library.
  • website: The current content of dagitty.net, including a version of the GUI (which may be older than the one in gui.
  • doc: LaTeX source of the dagitty PDF documentation.

Running the web interface locally

Clone the repository and open the file gui/dags.html in your web browser. Currently most functionality should work locally, but you will need an internet connection if you want to load or save DAG models on dagitty.net.

Running the R package

The R package can be installed from CRAN, but this version is not updated very frequently. If you want to install the most recent version of the dagitty R package, you can:

install.packages("remotes") # unless you have it already
remotes::install_github("jtextor/dagitty/r")

If you encounter any problems installing the R package, it is probably not due to dagitty itself, but due to the package "V8" that it depends on. I may try to remove this dependency in a future version.

More information

You can get more information on dagitty at dagitty.net and dagitty.net/learn. The R package is documented through the standard R help interface. There are also a few papers available:

  1. Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H. (2017). Robust causal inference using directed acyclic graphs: the R package ‘dagitty.’ In International Journal of Epidemiology (p. dyw341). Oxford University Press (OUP). https://doi.org/10.1093/ije/dyw341

  2. Ankan, A., Wortel, I. M. N., & Textor, J. (2021). Testing Graphical Causal Models Using the R Package “dagitty.” In Current Protocols (Vol. 1, Issue 2). Wiley. https://doi.org/10.1002/cpz1.45