Domain > dagitty.computational-immunology.org

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2025-01-08	159.69.32.4 (ClassC)

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!DOCTYPE html>html langen>head>	meta charsetutf-8>    meta http-equivX-UA-Compatible contentIEedge>	title>DAGitty - drawing and analyzing causal diagrams (DAGs)/title>	meta nameviewport contentwidthdevice-width, initial-scale1 />link relstylesheet hrefhttps://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css integritysha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh crossoriginanonymous>link relstylesheet typetext/css  hrefcontent.css/>/head>body>div classcontainer>h1>DAGitty — draw and analyze causal diagrams/h1>div classrow>div classcol-lg-8>       p classlead>        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 a hreflearn/index.html>learn/a> page.      /p>	div classcard-group>	div classcard>		div classcard-header>			h4 classcard-title>Launch/h4>		/div>		div classcard-body>		p classcard-text>		a hrefdags.html>img srcimages/launch.png alt />br/>		Launch DAGitty online in your browser/a>.		/p>		/div>	/div>	div classcard>		div classcard-header>			h4 classcard-title>Download/h4>		/div>		div classcard-body>		p classcard-text> 		a hrefdagitty.zip>img srcimages/down.png alt />br/>			Download DAGittys source for offline use/a>.		/p>		/div>	/div>	div classcard>		div classcard-header>			h4 classcard-title>Learn/h4>		/div>		div classcard-body>			p classcard-text>				a hreflearn/index.html>img srcimages/doc.png alt />br/>				Learn more about DAGs and DAGitty/a>.			/p>		/div>	/div>	div classcard>		div classcard-header>			h4 classcard-title>Code/h4>		/div>		div classcard-body>			p classcard-text>				a hrefhttps://github.com/jtextor/dagitty>					img srcimages/Rlogo.png alt styleheight:70px />/a>br/>				The R package dagitty is available on 				a hrefhttps://cran.r-project.org/web/packages/dagitty/index.html>CRAN/a> or 				a hrefhttps://github.com/jtextor/dagitty>github/a>.			/p>		/div>	/div>	/div>      p>       a namefeedback>DAGitty/a> is developed and maintained by        a hrefhttp://johannes-textor.name>Johannes Textor/a>        (a hrefhttps://ru.nl/icis>Institute for Computing and 		Information Sciences/a>,	a hrefhttps://www.ru.nl>Radboud University/a>, and 	Medical BioSciences department, a hrefhttps://www.radboudumc.nl/>Radboudumc/a>,	Nijmegen, The Netherlands). 	/p>	p>Many algorithms	implemented in DAGitty were developed in close collaboration with	a hrefhttps://www.tcs.uni-luebeck.de/en/mitarbeiter/liskiewi/>Maciej Liśkiewicz/a> 	and a hrefhttps://www.tcs.uni-luebeck.de/en/mitarbeiter/vanderzander/>Benito van der Zander/a>, University of Lübeck, Germany (see literature references below).      /p>	p>		DAGitty development happens 		on a hrefhttps://github.com/jtextor/dagitty>github/a>. You can		download all source code from there and also get involved. 	/p>      h2>How can I get help?/h2>	p>      p>         If you encounter any problems using DAGitty, or would like to have a certain         feature implemented, write to me on a hrefhttps://mastodon.social/@johannes_textor>Mastodon/a>, 	post on a hrefhttps://github.com/jtextor/dagitty>github/a> 		 or write to em>johannes {dot} textor {at} gmx {dot}          de./em> Your feedback and bug reports are very welcome and contribute to          making DAGitty a better experience for everyone.		 Past contributors are acknowledged in the a hrefmanual-3.x.pdf>manual/a>.      /p>      h2>Is it free?/h2>      p>        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          a hrefhttp://www.gnu.org/licenses/gpl.html>GNU general public license/a>.         Enjoy!      /p>	p>		DAGitty development has been sponsored by the Leeds Institute for		Data Analytics and by the Deutsche Forschungsgemeinschaft (DFG),		grant a hrefhttp://gepris.dfg.de/gepris/projekt/273587939>273587939/a>.br/>	/p>	p>		img stylewidth:20em srcimages/Dfg_logo_schriftzug_blau.jpg/>	/p>      h2>How can I cite DAGitty?/h2>	p>		If you use DAGitty in your scientific work, please consider citing us:	/p>	  	p>		Johannes Textor, Benito van der Zander, Mark K. Gilthorpe, Maciej Liskiewicz,		George T.H. Ellison. br/>		a hrefhttp://dx.doi.org/10.1093/ije/dyw341>Robust causal inference using directed acyclic graphs: the R package dagitty./a>br />		i>International Journal of Epidemiology/i> 45(6):1887-1894, 2016.br/>		a hrefhttp://johannes-textor.name/papers/2017-ije.pdf>PDF postprint/a>	/p>      h2>How can I learn more about how DAGitty works?/h2>      p>The algorithms used in DAGitty are described in more depth the following         papers:/p>      p>Johannes Textor, Maciej Liśkiewicz.br />a hrefhttp://www.tcs.uni-luebeck.de/downloads/papers/2011/Textor_Liskiewicz_Adjustment_Criteria_in_Causal_Diagrams_An_Algorithmic_Perspective.pdf>Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective./a>br />In i>Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)/i>, pp. 681-688. AUAI press, 2011.	  /p>	  	  p>Benito van der Zander, Maciej Liśkiewicz, Johannes Textor.br />a hrefhttp://auai.org/uai2014/proceedings/individuals/286.pdf>Constructing Separators and Adjustment Sets in Ancestral Graphs./a>br/>In em>Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014)/em>,	pp. 907-916. AUAI Press, 2014.	  /p>	p>Benito van der Zander, Johannes Textor, Maciej Liśkiewicz.br />a hrefhttp://ijcai.org/papers15/Papers/IJCAI15-457.pdf>Efficiently Finding Conditional Instruments for Causal Inference./a>br />	Inem> Proceedings of the 24th International Joint Conference on Artificial 	Intelligence (IJCAI 2015)/em>, pp. 3243-3249.	AAAI Press, 2015.	 /p>	p>Benito van der Zander, Maciej Liśkiewicz.br />	a hrefhttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12363/12096>Separators and Adjustment Sets in Markov Equivalent DAGs./a>br />	Inem> Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence 		(AAAI 2016)/em>, pp. 3315-3321. AAAI Press, 2016.	 /p>		  h2>What other related software is out there?/h2>	  p>There is currently quite a lot of activity in causal inference software. A few links:/p>	  	  ul>		li>a hrefhttps://cran.r-project.org/web/packages/ggdag/vignettes/intro-to-ggdag.html>ggdag/a>			is a nice R package based on dagitty			but tidyverse-compatible and with much better plotting functionality./li>		li>a hrefhttps://www.gerkelab.com/project/shinydag/>shinydag/a> is another GUI			aimed at visualizing DAGs and exporting them in different publication-ready formats./li>		li>a hrefhttp://www.phil.cmu.edu/projects/tetrad/>TETRAD/a>/li>		li>a hrefhttp://epi.dife.de/dag/>DAG program/a> /li>		li>a hrefhttp://journals.lww.com/epidem/Fulltext/2010/07000/dagR__A_Suite_of_R_Functions_for_Directed_Acyclic.26.aspx>dagR/a> /li>		li>a hrefhttps://cbdrh.shinyapps.io/daggle/>daggle/a> is a shiny app where you can practice the rules of DAG-based covariate selection./li>		li>a hrefhttps://github.com/krassowski/jupyterlab-dagitty>A JupyterLab extension to render dagitty models/a>/li>	  /ul>	  	  p>Please contact me if you know of other programs that should be added to this list,		or directly submit a pull request on github./p>/div>div classcol-sm-4 stylebackground-color: #f5f3f3;>!--	        h2>Versions/h2>      p>The following versions of DAGitty are available:/p>      ul>        li>a hrefdevelopment/dags.html>Development version/a> br />	  Recent development snapshot. May contain new 	  features, but could also contain new bugs./li>        li>a hrefexperimental>Experimental version/a> br />	  Most recent development snapshot. May not even work./li>        li>a hrefdags.html>3.0: Released 2019-01-09/a>/li>        li>a hrefhistory/v2.3/dags.html>2.3: Released 2015-08-19/a>/li>        li>a hrefhistory/v2.2/dags.html>2.2: Released 2014-10-30/a>/li>        li>a hrefhistory/v2.1/dags.html>2.1: Released 2014-02-06/a>/li>        li>a hrefhistory/v2.0/dags.html>2.0: Released 2013-02-12/a>/li>        li>a hrefhistory/v1.1/dags.html>1.1: Released 2011-11-29/a>/li>        li>a hrefhistory/v1.0/dags.html>1.0: Released 2011-03-24/a>/li>        li>a hrefhistory/v0.9b/dags.html>0.9b: Released 2010-11-24/a>/li>        li>a hrefhistory/v0.9a/dags.html>0.9a: Released 2010-09-01/a>/li>      /ul>-->iframe srchttps://mastodon.social/@johannes_textor/111198655416786580/embed classmastodon-embed stylemax-width: 100%; border: 0 width400 allowfullscreenallowfullscreen>/iframe>script srchttps://mastodon.social/embed.js asyncasync>/script>      h2>Changelog/h2>      p>strong>2023-10-07/strong>/p>      p> Moved to a new webserver after 12 years.	/p>      p>strong>2023-07-11/strong>/p>      p> Version 3.1 is out, featuring selection variables.	/p>      p>strong>2020-01-09/strong>/p>      p> Version 3.0 has been released! Complete reimplementation of the interface,		should work with mobile/touch now./p>      p>strong>2018-04-04/strong>/p>      p> Updated the development version and preparing for a long overdue release! /p>      p>strong>2015-08-19/strong>/p>      p>		Version 2.3 has been released! The most notable new feature: 		instrumental variables.      /p>      p>strong>2014-10-30/strong>/p>      p>		Version 2.2 has been released!       /p>      p>strong>2014-10-05/strong>/p>      p>	Version 2.2 is forthcoming and now available as the 	a hrefdevelopment/dags.html>Development version/a>. This version features	a new, SEM-like diagram drawing style and the ability to share your DAGs 	online.      /p>      p>strong>2014-04-14/strong>/p>      p>	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.      /p>	 p>a hrefchangelog.html>View older entries .../a>/p>/div>/div>!--row-->/div>!--container-->/body>/html>

Subdomains

Date	Domain	IP
gitlab.computational-immunology.org	2024-09-10	159.69.32.4
www.computational-immunology.org	2025-01-08	159.69.32.4
gateway.computational-immunology.org	2024-09-08	159.69.32.4
dagitty.computational-immunology.org	2025-01-08	159.69.32.4

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