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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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DTSTART;TZID=Asia/Seoul:20211112T110000
DTEND;TZID=Asia/Seoul:20211112T120000
DTSTAMP:20260427T090915
CREATED:20211111T170000Z
LAST-MODIFIED:20211111T084242Z
UID:5185-1636714800-1636718400@www.ibs.re.kr
SUMMARY:Detecting and quantifying causal associations in large nonlinear time series datasets
DESCRIPTION:We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”\, Runge et al.\, Science Advances\, 2019 \nIdentifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here\, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power\, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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