Results 1 to 10 of about 47,050 (138)
Causal machine learning for healthcare and precision medicine [PDF]
Royal Society Open Science, 2022 Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an ...
Pedro Sanchez +5 more
doaj +6 more sources
Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning
Ocean-Land-Atmosphere Research, 2023 It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness ...
X. San Liang, Dake Chen, Renhe Zhang
doaj +2 more sources
Causal ML: Python package for causal inference machine learning
SoftwareX, 2023 “Causality” is a complex concept that is based on roots in almost all subject areas and aims to answer the “why” question. Causal inference is one of the important branches of causal analysis, which assumes the existence of relationships between ...
Yang Zhao, Qing Liu
doaj +2 more sources
How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. [PDF]
PLoS ONE, 2023 We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer.
Henrika Langen, Martin Huber
doaj +4 more sources
Predicting preventable hospital readmissions with causal machine learning. [PDF]
Health Serv Res, 2020 AbstractObjectiveTo assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).Data SourcesElectronic health records maintained by Kaiser Permanente Northern California (KPNC ...
Marafino BJ +4 more
europepmc +4 more sources
Causal inference and machine learning in endocrine epidemiology
Endocrine JournalWith the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes.
Kosuke Inoue
doaj +3 more sources
Causality, Machine Learning, and Feature Selection: A Survey
SensorsCausality, which involves distinguishing between cause and effect, is essential for understanding complex relationships in data. This paper provides a review of causality in two key areas: causal discovery and causal inference.
Asmae Lamsaf +3 more
doaj +3 more sources
Causality for Machine Learning [PDF]
, 2022 Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way.
openaire +4 more sources
Causal connections between socioeconomic disparities and COVID-19 in the USA
Scientific Reports, 2022 With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance.
Tannista Banerjee +3 more
doaj +1 more source
Research Trend of Causal Machine Learning Method: A Literature Review
IJID (International Journal on Informatics for Development), 2020 Machine learning is commonly used to predict and implement pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ...
Shindy Arti +2 more
doaj +1 more source