Results 11 to 20 of about 8,838,426 (323)

Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools. [PDF]

Front Microbiol
The human microbiome is increasingly recognized as a key mediator of health and disease, yet translating microbial associations into actionable interventions remains challenging.
Khelfaoui I, Wang W, Meskher H, Shehata AI, El Basuini MF, Abouelenein MF, Degha HE, Alhoshy M, Teiba II, Mahmoud SS.   +9 more
europepmc   +2 more sources

Causal machine learning for predicting treatment outcomes [PDF]

Nature Medicine
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.
S. Feuerriegel, Dennis Frauen, Valentyn Melnychuk, J. Schweisthal, Konstantin Hess, Alicia Curth, S. Bauer, Niki Kilbertus, Isaac S. Kohane, M. van der Schaar   +9 more
semanticscholar   +7 more sources

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Frontiers in Bioinformatics, 2021
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation.
Paola Lecca
doaj   +3 more sources

Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials. [PDF]

Thorax, 2023
Rationale Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention.
Verstraete K, Gyselinck I, Huts H, Das N, Topalovic M, De Vos M, Janssens W.   +6 more
europepmc   +2 more sources

STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks [PDF]

Frontiers in Microbiology
The reproducibility crisis in causal microbiome research necessitates robust validation frameworks. Current studies often face inconsistent validation methods, limited interpretability, and a lack of standardized reporting, creating a gap in reliable ...
Issam Khelfaoui, Wenxin Wang, Akram Ismael Shehata, Akram Ismael Shehata, Akram Ismael Shehata, Hicham Meskher, Mohammed F. El Basuini, Abdalla M. A. Mohamed, Mohamed F. Abouelenein, Houssem Eddine Degha, Mayada Alhoshy, Islam I. Teiba, Omnia Mahmoud, Seedahmed S. Mahmoud   +13 more
doaj   +2 more sources

Estimating causal effects with machine learning: A guide for ecologists

Methods in Ecology and Evolution
In ecology, there is a growing need to move beyond correlations to uncovering causal effects from observational data. With the parallel increase in big data and machine learning algorithms, the opportunity now exists to benefit from causal machine ...
Suchinta Arif
doaj   +2 more sources

Causal machine learning for single-cell genomics

Nature Genetics
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole ...
Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer, Hananeh Aliee, Yoshua Bengio, F. Theis   +5 more
semanticscholar   +4 more sources

Causal Machine Learning in Marketing

International Journal of Business & Management Studies
This study reviews three primary purposes of causal machine learning (CML) in marketing, merging impact evaluation of marketing interventions with machine learning algorithms for learning statistical patterns from data. Firstly, CML enables more credible
Martin Huber
semanticscholar   +3 more sources

Causal machine learning for sustainable agroecosystems

CoRR
In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learning (
Vasileios Sitokonstantinou, Emiliano D'iaz Salas Porras, Jordi Cerdà-Bautista, M. Piles, Ioannis N. Athanasiadis, Hannah Kerner, Giulia Martini, Lily-belle Sweet, Ilias Tsoumas, J. Zscheischler, G. Camps-Valls   +10 more
semanticscholar   +3 more sources

Causal inference and machine learning in endocrine epidemiology

Endocrine Journal
With 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