drawing and analyzing causal diagrams (DAGs)

DAGitty is a browser-based environment for creating, editing, and analyzing
causal diagrams (also known as directed acyclic graphs or causal Bayesian networks).
The focus is on the use of causal diagrams for minimizing bias in empirical
studies in epidemiology and other disciplines. For background information, see
the “learn” page.

DAGitty is developed and maintained by
Johannes Textor
(Institute for Computing and
Information Sciences,
Radboud University, and
Medical BioSciences department, Radboudumc,
Nijmegen, The Netherlands).

Many algorithms
implemented in DAGitty were developed in close collaboration with
Maciej Liśkiewicz
and Benito van der Zander, University of Lübeck, Germany (see literature references below).

DAGitty development happens
on github. You can
download all source code from there and also get involved.

How can I get help?

If you encounter any problems using DAGitty, or would like to have a certain
feature implemented, write to me on Mastodon,
post on github
or write to “johannes {dot} textor {at} gmx {dot}
de”. Your feedback and bug reports are very welcome and contribute to
making DAGitty a better experience for everyone.
Past contributors are acknowledged in the manual.

Is it free?

Because the main purpose of DAGitty is facilitating the use of causal models
in empirical studies, it is and will always be Free software (both
“free as in beer” and “free as in speech”). You can copy, redistribute, and
modify it under the terms of the
GNU general public license.
Enjoy!

DAGitty development has been sponsored by the Leeds Institute for
Data Analytics and by the Deutsche Forschungsgemeinschaft (DFG),
grant 273587939.

How can I cite DAGitty?

If you use DAGitty in your scientific work, please consider citing us:

Johannes Textor, Benito van der Zander, Mark K. Gilthorpe, Maciej Liskiewicz,
George T.H. Ellison.
Robust causal inference using directed acyclic graphs: the R package ‘dagitty’.
International Journal of Epidemiology 45(6):1887-1894, 2016.
PDF postprint

How can I learn more about how DAGitty works?

The algorithms used in DAGitty are described in more depth the following
papers:

Johannes Textor, Maciej Liśkiewicz.
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective.
In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 681-688. AUAI press, 2011.

Benito van der Zander, Maciej Liśkiewicz, Johannes Textor.
Constructing Separators and Adjustment Sets in Ancestral Graphs.
In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014),
pp. 907-916. AUAI Press, 2014.

Benito van der Zander, Johannes Textor, Maciej Liśkiewicz.
Efficiently Finding
Conditional Instruments for Causal Inference.
In Proceedings of the 24th International Joint Conference on Artificial
Intelligence (IJCAI 2015), pp. 3243-3249.
AAAI Press, 2015.

Benito van der Zander, Maciej Liśkiewicz.
Separators and Adjustment Sets in Markov Equivalent DAGs.
In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
(AAAI 2016), pp. 3315-3321. AAAI Press, 2016.

What other related software is out there?

There is currently quite a lot of activity in causal inference software. A few links:

Please contact me if you know of other programs that should be added to this list,
or directly submit a pull request on github.

Changelog

2023-10-07

Moved to a new webserver after 12 years.

2023-07-11

Version 3.1 is out, featuring selection variables.

2020-01-09

Version 3.0 has been released! Complete reimplementation of the interface,
should work with mobile/touch now.

2018-04-04

Updated the development version and preparing for a long overdue release!

2015-08-19

Version 2.3 has been released! The most notable new feature:
instrumental variables.

2014-10-30

Version 2.2 has been released!

2014-10-05

Version 2.2 is forthcoming and now available as the
Development version. This version features
a new, SEM-like diagram drawing style and the ability to share your DAGs
online.

2014-04-14

At “dagitty.net/learn”, I am building some interactive tutorials
using the forthcoming version 2.1 of DAGitty. That version will
be embeddable into HTML pages, which will make it easy to include
interactive DAG drawings into just about any webpage. Check it out!
The first examples include an implementation of the “Simpson Machine”
and an interactive version of a tutorial text on d-separation.

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