]> 2026-04-04T20:34:21+02:00 Publications:Incremental causal discovery and visualization 500 en <p>Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.</p> 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019 http://dx.doi.org/10.1145/3304079.3310287 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1391292 Proceedings of the Workshop on Interactive Data Mining, WIDM 2019 Conference Paper Incremental causal discovery and visualization 2019 2020-02-04T21:22:28Z 2458884.3906019 Incremental causal discovery and visualization 500 0 plainlist list 1 [[:Publications:Incremental causal discovery and visualization]] Incremental causal discovery and visualization# QUERY19e14aa12189dbd7b68e538384b29e96 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Incremental causal discovery and visualization# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Incremental causal discovery and visualization# ERRe24f17e090c98448f3de99d03182b7b6