LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients
Sign Up to like & getrecommendations! Published in 2024 at "Neural Processing Letters"
DOI: 10.1007/s11063-024-11436-z
Abstract: The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables,… read more here.
Keywords: lecasim learning; learning causal; structure; structure via ... See more keywords
Agency at a distance: learning causal connections
Sign Up to like & getrecommendations! Published in 2024 at "Phenomenology and the Cognitive Sciences"
DOI: 10.1007/s11097-024-09992-9
Abstract: In a series of papers, we have argued that causal cognition has coevolved with the use of various tools. Animals use tools, but only as extensions of their own bodies, while humans use tools that… read more here.
Keywords: agency distance; distance; learning causal; causal connections ... See more keywords
Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice
Sign Up to like & getrecommendations! Published in 2024 at "Gut Microbes"
DOI: 10.1080/19490976.2024.2388805
Abstract: ABSTRACT Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life. It is unclear… read more here.
Keywords: learning causal; gut; machine learning; bile acid ... See more keywords
Multi-dimensional analysis of dairy production costs and optimization of consumption reduction strategies based on deep learning and causal reasoning
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DOI: 10.1109/access.2025.3628931
Abstract: Dairy production systems face increasing challenges in cost optimization due to fluctuating resource availability, environmental constraints, and market volatility. Traditional modeling approaches struggle to capture the complex, dynamic, and interdependent nature of dairy operations. To… read more here.
Keywords: deep learning; dairy production; learning causal; causal reasoning ... See more keywords
Learning Causal Structures Based on Divide and Conquer.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.3010004
Abstract: This article addresses two important issues of causal inference in the high-dimensional situation. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another… read more here.
Keywords: structures based; divide conquer; based divide; causal structures ... See more keywords
Learning causal networks with latent variables from multivariate information in genomic data
Sign Up to like & getrecommendations! Published in 2017 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1005662
Abstract: Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models… read more here.
Keywords: information; genomic data; causal networks; latent variables ... See more keywords