About Open Causal
Let's make FAIR assumptions!
Causal graphs are essential to causal inquiry in many fields, including epidemiology, economics and computer science. Using the graph to present causal assumptions is strongly recommended, yet their adoption remains limited. Even when reported, they are only available in image format. Currently, there is no platform for researchers to share causal graphs, which hinders their reusability and makes it difficult to build upon existing knowledge.
We introduce Open Causal, a platform to facilitate sharing, discussion, and exploration of causal graphs. Open Causal will function as an open registry for causal graphs, where researchers can publish their graphs in machine-readable format with an open licence and receive a DOI. Other researchers can search, comment, clone and modify existing graphs, fostering an open discussion on assumptions and enabling reuse of the models. The platform facilitates community-driven curation and iterative evolution of graphs. An API allows integration with popular statistical packages in Python and R. Additionally, the platform supports empirical validation, enabling researchers to test causal assumptions against their datasets without uploading sensitive data. The platform also integrates AI-powered tools for advanced use cases.
Open Causal transforms once immutable supplementary materials into interactive open research objects. It fosters the adoption of FAIR principles (findability, accessibility, interoperability, reusability) in causal inference.
How to Cite Open Causal
If you use Open Causal in your research, please cite the platform as well as any specific causal graphs you refer to. Proper citation helps give credit to the creators and allows others to find and build upon existing knowledge. Please include the Digital Object Identifier (DOI) provided for each graph that makes it permanently findable and trackable.
Citing the Open Causal:
Küçükali, H. (2026). Open Causal: An open platform for causal graphs. Journal of Epidemiology and Community Health, 80, A179. https://doi.org/10.1136/jech-2026-SSMabstracts.359
Citing specific causal graphs, for instance in APA style:
Author(s). (Year). Title of the causal graph. Open Causal. https://doi.org/10.83031/XXXXXXXCitation format in common styles are provided in the sidebar of each graph page.
Events and Demonstrations
We regularly present Open Causal at conferences and workshops. If you would like us to present at your event, please contact us.
Upcoming Events
No upcoming events at the moment.
Past Events
| Date | Event | Location | Link |
|---|---|---|---|
| 15 Sep 2026 | UU Pharmacoepidemiology Group Seminar | Utrecht, NL | Link |
| 9-11 Sep 2026 | EuroEpi 2026 / 70th Society for Social Medicine & Population Health Annual Conference | London, UK | Link |
| 25 Jun 2026 | EpiCon | Enschede, NL | Link |
| 22 May 2026 | Applied Causal Graphs Workshop | Potsdam, DE | Link |
| 11 May 2026 | Epidemiology Department Seminar Series | Rotterdam, NL | Link |
| 27-29 Apr 2026 | Global Exposome Summit | Sitges, ES | Link |
| 2 Mar 2026 | Causal Inference in AI Meetup | Rotterdam, NL | — |
| 19-23 Jan 2026 | Foundations of causal inference workshop | Cambridge, UK | Link |
| 10 Dec 2025 | Causal Inference Group seminar series | Rotterdam, NL | Link |
| 7 Dec 2025 | EurIPS Causality for Impact Workshop | Copenhagen, DK | Link |
| 13 Nov 2025 | Causal Data Science Meeting | Online | Link |
| 13 Nov 2025 | European Public Health Conference | Helsinki, Finland | Link |
Repository Registry
Open Causal is registered in re3data.org, a global registry of research data repositories recognized for their commitment to FAIR principles and research data management.
