Results 11 to 20 of about 47,050 (138)
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
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Causal Machine Learning and its use for public policy
Swiss Journal of Economics and Statistics, 2023 In recent years, microeconometrics experienced the ‘credibility revolution’, culminating in the 2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens.
Michael Lechner
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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
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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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Machine Learning and Causality [PDF]
IMF Working Papers, 2019 Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality.
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Causality, machine learning and human insight
Analytica Chimica Acta, 2023 Modern instruments generate BIG DATA that require information extraction before they can be used. A hybrid modelling framework for that is presented and illustrated. Its purpose is to convert meaningless data to meaningful information and to contribute to a theoretical, practical, and democratic basis for tomorrow's handling of BIG DATA in science and ...
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Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine
IEEE Access, 2021 A feedforward neural network with random weights (RW-FFNN) uses a randomized feature map layer. This randomization enables the optimization problem to be replaced by a standard linear least-squares problem, which offers a major advantage in terms of ...
Akira Sasou
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Recent Developments in Causal Inference and Machine Learning
Annual Review of Sociology, 2022 This paper provides an updated review of the latest advances in causal inference in sociology and other disciplines. We focus on four topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We show how machine learning, as an estimation strategy, can be
Jennie E. Brand, Xiang Zhou, Yu Xie
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A Double machine learning trend model for citizen science data
Methods in Ecology and Evolution, 2023 Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure ...
Daniel Fink +10 more
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Physical Grounds for Causal Perspectivalism
Entropy, 2023 We ground the asymmetry of causal relations in the internal physical states of a special kind of open and irreversible physical system, a causal agent.
Gerard J. Milburn +2 more
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