Results 41 to 50 of about 47,050 (138)

MACHINE LEARNING AND ECONOMETRICS: BRIDGING THE GAP FOR ENHANCED ECONOMIC ANALYSIS

مجلة الغري للعلوم الاقتصادية والادارية
This paper explores the integration of machine learning techniques in econometric analysis, emphasizing the transformative impact on economic research.
Jamiu Adeniyi Yusuf, Abdulkadri IDRIS , Azeez Olawale AKINLOLU   +2 more
doaj   +1 more source

Comparing XGBoost and Double Machine Learning for Predicting the Nitrogen Requirement of Rice

Remote Sensing
Estimating how crop yield responds to site-specific nitrogen (N) fertilization is essential for maximizing yield potential under variable field conditions.
Miltiadis Iatrou, Spiros Mourelatos, Christos Karydas   +2 more
doaj   +1 more source

Utilizing deep learning-based causal inference to explore vancomycin’s impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients

Microbiology Spectrum
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin.
Min Woo Kang, Yoonjin Kang
doaj   +1 more source

Causal machine learning for single-cell genomics

Nature Genetics
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies allow for measuring the effect of targeted perturbations on the whole transcriptome.
Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer, Hananeh Aliee, Yoshua Bengio, Fabian J. Theis   +5 more
openaire   +3 more sources

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Journal of Geophysical Research: Machine Learning and Computation
Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science.
J. Jake Nichol, Michael Weylandt, G. Matthew Fricke, Melanie E. Moses, Diana Bull, Laura P. Swiler   +5 more
doaj   +1 more source

Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models

Biomedicines
Background/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
doaj   +1 more source

Robust double machine learning model with application to omics data

BMC Bioinformatics
Background Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in
Xuqing Wang, Yahang Liu, Guoyou Qin, Yongfu Yu   +3 more
doaj   +1 more source

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice

Gut Microbes
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.
Wanling Chen, Peng Zhang, Xueli Zhang, Tiantian Xiao, Jianhai Zeng, Kaiping Guo, Huixian Qiu, Guoqiang Cheng, Zhangxing Wang, Wenhao Zhou, Shujuan Zeng, Mingbang Wang   +11 more
doaj   +1 more source

Causal machine learning for predicting treatment outcomes

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. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual ...
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar   +9 more
openaire   +5 more sources

Quantitative causality, causality-guided scientific discovery, and causal machine learning

Comment: 10 pages, 3 figures. To appear in Ocean-Land-Atmosphere Research.
X. San Liang, X San Liang, Dake Chen, Renhe Zhang   +3 more
openaire   +3 more sources