[2010.06164] Causal Structure Learning: a Bayesian approach based on random graphs Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2010.06164 (cs) [Submitted on 13 Oct 2020] Title: Causal Structure Learning: a Bayesian approach based on random graphs Authors: Mauricio Gonzalez-Soto , Ivan R. Feliciano-Avelino , L. Enrique Sucar , Hugo J. Escalante Balderas View a PDF of the paper titled Causal Structure Learning: a Bayesian approach based on random graphs, by Mauricio Gonzalez-Soto and 3 other authors View PDF HTML (experimental) Abstract: A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2010.06164 [cs.AI] (or arXiv:2010.06164v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2010.06164 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/s00521-021-06506-5 Focus to learn more DOI(s) linking to related resources Submission history From: Mauricio Gonzalez-Soto [ view email ] [v1] Tue, 13 Oct 2020 04:13:06 UTC (2,937 KB) Full-text links: Access Paper: View a PDF of the paper titled Causal Structure Learning: a Bayesian approach based on random graphs, by Mauricio Gonzalez-Soto and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2020-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Mauricio Gonzalez-Soto Luis Enrique Sucar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from