Abstract: The kernel-based inverse system identification framework enables accurate identification of systems with non-minimum phase dynamics, greatly expanding the potential of non-causal system ...
Introduction The underlying causes of supply-side and demand-side challenges in immunisation are poorly understood, leading to symptomatic solutions. This study engaged stakeholders to develop ...
A slow but significant change has occurred in how healthcare professionals and organizations are expected to respond when something has gone wrong in a patient’s care. In 2001, the US accreditation ...
ABSTRACT: This paper applies the interdisciplinary framework of Earth System Science (ESS) to analyze the complex socio-ecological crisis facing the agricultural sector in the Hashemite Kingdom of ...
Linear GTM models can’t explain stalled buyer decisioning or collapsing performance. A causal GTM logic layer is emerging to restore clarity and control. Scott Brinker’s Martech for 2026 report offers ...
Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that making causal inferences about biological processes (e.g. illness) depends on the ...
OpenASCE (Open All-Scale Casual Engine) is a comprehensive, easy-to-use, and efficient end-to-end large-scale causal learning system. It provides causal discovery, causal effect estimation, and ...
Abstract: Currently, various rapidly developing information technologies are gradually transforming traditional social systems into Complex Social Systems (CSS). On the one hand, individuals' ability ...
While there is growing consensus that real-world data should play a larger role in generating causal evidence for health care, it is less clear whether and how AI can help. Current approaches to ...
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