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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250912T140000
DTEND;TZID=Asia/Seoul:20250912T160000
DTSTAMP:20260423T030449
CREATED:20250825T081619Z
LAST-MODIFIED:20250910T002342Z
UID:11438-1757685600-1757692800@www.ibs.re.kr
SUMMARY:Decomposing causality into its synergistic\, unique\, and redundant components - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Decomposing causality into its synergistic\, unique\, and redundant components” by Álvaro Martínez-Sánchez et al.\, Nature Communications\, 2024. \nAbstract \nCausality lies at the heart of scientific inquiry\, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role\, current methods for causal inference face significant challenges due to nonlinear dependencies\, stochastic interactions\, self-causation\, collider effects\, and influences from exogenous factors\, among others. While existing methods can effectively address some of these challenges\, no single approach has successfully integrated all these aspects. Here\, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant\, unique\, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations\, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.
URL:https://www.ibs.re.kr/bimag/event/data-driven-model-discovery-and-model-selection-for-noisy-biological-systems-olive-cawiding/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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