AIMC Topic: Causality

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Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity.

BMC public health Mar 8, 2025
BACKGROUND: Complex public health problems have been addressed in communities through systems thinking and participatory methods like Group Model Building (GMB) and Causal Loop Diagrams (CLDs) albeit with some challenges. This study aimed to explore ...
Causality Natural Language Processing Semantics Humans Child Victoria
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DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Neural networks : the official journal of the International Neural Network Society Dec 30, 2024
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text ins...
Decision Making Semantics Language Causality Vision, Ocular Neural Networks, Computer Algorithms Humans
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Utilising causal inference methods to estimate effects and strategise interventions in observational health data.

PloS one Dec 30, 2024
Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RCTs are not feasible, large observational data sets emerge as a pivotal resource, though these data s...
Young Adult Observational Studies as Topic Female Adult Middle Aged Humans Male Australia Aged Causality Machine Learning
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Modeling document causal structure with a hypergraph for event causality identification.

Neural networks : the official journal of the International Neural Network Society Dec 28, 2024
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neura...
Neural Networks, Computer Algorithms Causality Humans Semantics
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Machine learning in causal inference for epidemiology.

European journal of epidemiology Nov 13, 2024
In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimate...
Epidemiologic Methods Epidemiology Models, Statistical Likelihood Functions Machine Learning Causality Humans
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Causality and scientific explanation of artificial intelligence systems in biomedicine.

Pflugers Archiv : European journal of physiology Oct 29, 2024
With rapid advances of deep neural networks over the past decade, artificial intelligence (AI) systems are now commonplace in many applications in biomedicine. These systems often achieve high predictive accuracy in clinical studies, and increasingly...
Artificial Intelligence Biomedical Research Causality Humans
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Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.

Accident; analysis and prevention Oct 7, 2024
Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effecti...
Time Factors Machine Learning Cities China Humans Causality Accidents, Traffic Environment Design
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Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Epidemiology (Cambridge, Mass.) Aug 16, 2024
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that...
Models, Statistical Humans Epidemiologic Methods Machine Learning Reproducibility of Results Algorithms Computer Simulation Causality
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Solving the puzzle of quality of life in cancer: integrating causal inference and machine learning for data-driven insights.

Health and quality of life outcomes Aug 7, 2024
BACKGROUND: Understanding the determinants of global quality of life in cancer patients is crucial for improving their overall well-being. While correlations between various factors and quality of life have been established, the causal relationships ...
Machine Learning Retrospective Studies Humans Neoplasms Quality of Life Causality Surveys and Questionnaires Adult Male Middle Aged Female Aged
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Causal inference and machine learning in endocrine epidemiology.

Endocrine journal Jul 6, 2024
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. However, studies on the effective a...
Humans Endocrinology Machine Learning Causality Endocrine System Diseases
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