{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:03:12Z","timestamp":1773802992218,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"25","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>In this paper we address the problem of discovering causal relationships from observational event sequence data.\nExisting methods typically assume that events are instantaneous point events, however in many real-world settings, events have duration.\nFor example, in healthcare, a patient's symptoms may persist over a time interval and influence clinical actions while ongoing.\nTo address this, we introduce a causal model for interval-based event sequences that captures rich causal structures, including interactions between events and causal mechanisms that depend on whether other events are ongoing.\nWe prove that our model is identifiable in the limit and present a practical causal discovery algorithm, Niagara, grounded in the algorithmic Markov condition.\nTo select among candidate models, we employ a minimum description length (MDL) criterion, enabling robust inference even with limited data.\nWe validate our approach on synthetic and real data and demonstrate its utility on a real-world medical case study, where it uncovers meaningful causal relationships from noisy, interval-based event data.<\/jats:p>","DOI":"10.1609\/aaai.v40i25.39201","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:16:50Z","timestamp":1773796610000},"page":"20642-20649","source":"Crossref","is-referenced-by-count":0,"title":["Causal Discovery from Interval-Based Event Sequences"],"prefix":"10.1609","volume":"40","author":[{"given":"L\u00e9na\u00efg","family":"Cornanguer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joscha","family":"C\u00fcppers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jilles","family":"Vreeken","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39201\/43162","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39201\/43162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:16:50Z","timestamp":1773796610000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i25.39201","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}