Items related to Machine Learning for Causal Inference
- PublisherSpringer
- Publication date2023
- ISBN 10 3031350502
- ISBN 13 9783031350504
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages314
- EditorLi Sheng, Chu Zhixuan
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Machine Learning for Causal Inference
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Machine Learning for Causal Inference
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Machine Learning for Causal Inference
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Machine Learning for Causal Inference
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Machine Learning for Causal Inference
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Machine Learning for Causal Inference
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Berlin Springer International Publishing Springer Nov 2023, 2023
ISBN 10: 3031350502
ISBN 13: 9783031350504
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data. 298 pp. Englisch. Seller Inventory # 9783031350504
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Machine Learning for Causal Inference
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Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031350502
ISBN 13: 9783031350504
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative exampl. Seller Inventory # 870565835
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