Results 31 to 40 of about 47,050 (138)
Inferring the heterogeneous effect of urban land use on building height with causal machine learning
GIScience & Remote SensingMachine learning has become an important approach for land use change modeling. However, conventional machine learning algorithms are limited in their ability to capture causal relationships in land use change, which are important knowledge for planners ...
Yimin Chen +6 more
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Machine Learning in Causal Inference: Application in Pharmacovigilance
Drug Safety, 2022 Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance.
Yiqing Zhao +6 more
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EntropyEmergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual ...
Bing Yuan +8 more
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Prescriptive maintenance with causal machine learning
, 2022 Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often imperfect in reality and does not make the asset as good as new. Although a variety of imperfect maintenance policies have been proposed in the literature, these rely on ...
Vanderschueren, Toon +4 more
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DoubleML: An Object-Oriented Implementation of Double Machine Learning in R
Journal of Statistical SoftwareThe R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
Philipp Bach +4 more
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Causal inference in AI education: A primer
Journal of Causal Inference, 2022 The study of causal inference has seen recent momentum in machine learning and artificial intelligence (AI), particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability (among others).
Forney Andrew, Mueller Scott
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Comprehensive Causal Machine Learning
Uncovering causal effects in multiple treatment setting at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity.
Lechner, Michael, Mareckova, Jana
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Causal hybrid modeling with double machine learning—applications in carbon flux modeling
Machine Learning: Science and TechnologyHybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and regularization biases pose challenges in hybrid modeling to achieve these ...
Kai-Hendrik Cohrs +4 more
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A causal artificial intelligence model for payment delay optimisation in supply chain financing
Supply Chain AnalyticsSupply chain financing (SCF) has become a popular approach for small- and medium-sized enterprises (SMEs) to improve financial resilience. Payment delays within SCF have emerged as a critical operational challenge for both suppliers and SCF providers ...
Lingxuan Kong, Alexandra Brintrup
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Causal inference and observational data
BMC Medical Research Methodology, 2023 Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from ...
Ivan Olier +3 more
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