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The impact of physical activity on psychological distress in young people: An analysis of longitudinal data from the Millennium Cohort Study using causal machine learning [PDF]
JCPP AdvancesBackground Physical activity is often associated with positive psychological wellbeing in adolescents, and there is evidence from randomised trials to support this. However, the effect sizes reported from intervention studies vary considerably. Moreover,
Lewis W. Paton +6 more
doaj +2 more sources
Causal Machine Learning: A Survey and Open Problems [PDF]
arXiv.org, 2022 Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
Jean Kaddour +4 more
semanticscholar +1 more source
Simple low cost causal discovery using mutual information and domain knowledge [PDF]
, 2011 PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the ...
Joseph, Adrian
core +4 more sources
Prescriptive maintenance with causal machine learning
CoRR, 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 ...
Toon Vanderschueren +4 more
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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 +3 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
Tuning Derivatives for Causal Fairness in Machine Learning
Machine LearningAbstract Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age. Classical fairness notions, most notably Statistical Parity (SP), demand that predictions be independent of the protected attributes, but are ...
Filip Edström +3 more
openaire +4 more sources
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
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Learning Causal Effects From Observational Data in Healthcare: A Review and Summary
Frontiers in Medicine, 2022 Causal inference is a broad field that seeks to build and apply models that learn the effect of interventions on outcomes using many data types.
Jingpu Shi, Beau Norgeot
doaj +1 more source
Engineering, 2020 Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang +9 more
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