Welcome to DAGitty!

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What is this?

DAGitty is a browser-based environment for creating, editing, and analyzing causal models (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 (Tumor Immmunology Lab and Institute for Computing and Information Sciences, Radboud University Nijmegen). The 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, please 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 purposoe 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.
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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.

Do similar programs exist?

Yes. None of these does exactly what DAGitty does, but they have overlapping functionalities.

Please contact me if you know of other programs that should be added to this list.


The following versions of DAGitty are available:

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


Version 2.2 has been released!


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.


At "daggity.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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