Applying causal discovery to single-cell analyses using CausalCell [PDF]
eLife, 2023 Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality.
Yujian Wen +7 more
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Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models [PDF]
BiomedicinesBackground/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes.
Mi Jin Noh, Yang Sok Kim
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A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyA representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task.
Francesco Canonaco +5 more
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AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways [PDF]
Frontiers in GeneticsIntroductionCardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases,
Adèle H. Ribeiro +7 more
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Mental health progress requires causal diagnostic nosology and scalable causal discovery [PDF]
Frontiers in Psychiatry, 2022 Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other ...
Glenn N. Saxe +4 more
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Correction: A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyFrancesco Canonaco +5 more
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Collective Causal Relations Discovery Algorithm for Multivariate Time-Series [PDF]
Jisuanji gongcheng, 2023 Causal discovery from multivariate time-series is a significant and fundamental problem in numerous disciplines.The existing multivariate time-series causal discovery methods learn the causal relations for each individual while some individuals may share
CAI Ruichu, WU Yunjin, CHEN Wei, HAO Zhifeng
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Semi-supervised Learning of Visual Causal Macrovariables
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Discovery of causally related concepts is one of the key challenges in extracting knowledge from observational data. Lower-dimensional “causal macrovariables” represent concepts which preserve all relevant causal information in high-dimensional systems ...
Aruna Jammalamadaka +6 more
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Environmental Data Science, 2022 Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always ...
Xavier-Andoni Tibau +5 more
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Mining Causality via Information Bottleneck [PDF]
Jisuanji kexue, 2022 Causal discovery from observational data is a fundamental problem in many disciplines.However,existing methods such as constraint-based methods and causal function-based methods have strong assumptions on the causal mechanism of data,and are only ...
QIAO Jie, CAI Rui-chu, HAO Zhi-feng
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