Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence.
Angeliki Papana
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The Eternal Relations of Origin, Causality, and Implications for Models of God
The classical doctrine of the eternal relations or origin (ERO) claims that these relations are (1) atemporal and (2) causal. In this paper, I investigate the casual nature of the ERO, highlighting that the patristic and medieval Christian thinkers who ...
Andrew Hollingsworth
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The COVID-19 Vaccine Safety Research Center: a cornerstone for strengthening safety evidence for COVID-19 vaccination in the Republic of Korea [PDF]
The COVID-19 Vaccine Safety Research Committee (CoVaSC) was established in November 2021 to address the growing need for independent, in-depth scientific evidence on adverse events (AEs) following coronavirus disease 2019 (COVID-19) vaccination.
Na-Young Jeong +14 more
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Agentic Recommender System Konzept für nachhaltiges Wissensmanagement
Unternehmen stehen vor erheblichen Herausforderungen beim Erhalt, bei der Nutzung und Weitergabe zentralen Wissens. Demografischer Wandel, zunehmender Mitarbeiterfluktuation und steigende organisatorische Komplexit ¨at erschweren klassische ...
Simon Wilbers +3 more
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The attribution of causality, perception of responsibility, and preferred generic means of life distribution [PDF]
The attribution of causality, a central concept in human cognition theory, is the principal instrument for investigating functional links between events and phenomena. Although the links between causality and moral responsibility are commonly recognized,
Okhrimenko Iana
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system.
Santtu Tikka +2 more
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From Causal Pairs to Causal Graphs
Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG ...
Rezaur Rashid +2 more
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Developmental Dyslexia, Reading Acquisition, and Statistical Learning: A Sceptic’s Guide
Many theories have been put forward that propose that developmental dyslexia is caused by low-level neural, cognitive, or perceptual deficits. For example, statistical learning is a cognitive mechanism that allows the learner to detect a probabilistic ...
Xenia Schmalz +2 more
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Introduction: India is witnessing a growing burden of non-communicable diseases (NCDs), with type 2 diabetes mellitus emerging as a major public health challenge. Gujarat, with its oil- and sugar-rich diet, ranks second nationally in diabetes prevalence.
Pratik M Makwana +3 more
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Causality and micro-causality in curved spacetime [PDF]
We consider how causality and micro-causality are realised in QED in curved spacetime. The photon propagator is found to exhibit novel non-analytic behaviour due to vacuum polarization, which invalidates the Kramers-Kronig dispersion relation and calls into question the validity of micro-causality in curved spacetime. This non-analyticity is ultimately
Timothy Hollowood, Graham Shore
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