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Machine learning for causal inference

Li, Sheng.

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Machine learning for causal inference

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for causal inference/ edited by Sheng Li, Zhixuan Chu.
其他作者:	Li, Sheng.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xvi, 298 p. :illustrations, digital ;24 cm.
內容註:	Overview of the Book -- Causal Inference Preliminary -- Causal Effect Estimation: Basic Methodologies -- Causal Inference on Graphs -- Causal Effect Estimation: Recent Progress, Challenges, and Opportunities -- Fair Machine Learning Through the Lens of Causality -- Causal Explainable AI -- Causal Domain Generalization -- Causal Inference and Natural Language Processing -- Causal Inference and Recommendations -- Causality Encourage the Identifiability of Instance-Dependent Label Noise -- Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data -- Continual Causal Effect Estimation -- Summary.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-031-35051-1
ISBN:	9783031350511

Machine learning for causal inference
Machine learning for causal inference[electronic resource] /edited by Sheng Li, Zhixuan Chu. - Cham :Springer International Publishing :2023. - xvi, 298 p. :illustrations, digital ;24 cm.

Overview of the Book -- Causal Inference Preliminary -- Causal Effect Estimation: Basic Methodologies -- Causal Inference on Graphs -- Causal Effect Estimation: Recent Progress, Challenges, and Opportunities -- Fair Machine Learning Through the Lens of Causality -- Causal Explainable AI -- Causal Domain Generalization -- Causal Inference and Natural Language Processing -- Causal Inference and Recommendations -- Causality Encourage the Identifiability of Instance-Dependent Label Noise -- Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data -- Continual Causal Effect Estimation -- Summary.

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.

ISBN: 9783031350511

Standard No.: 10.1007/978-3-031-35051-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M33 2023

Dewey Class. No.: 006.31

Machine learning for causal inference
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505 0 $a Overview of the Book -- Causal Inference Preliminary -- Causal Effect Estimation: Basic Methodologies -- Causal Inference on Graphs -- Causal Effect Estimation: Recent Progress, Challenges, and Opportunities -- Fair Machine Learning Through the Lens of Causality -- Causal Explainable AI -- Causal Domain Generalization -- Causal Inference and Natural Language Processing -- Causal Inference and Recommendations -- Causality Encourage the Identifiability of Instance-Dependent Label Noise -- Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data -- Continual Causal Effect Estimation -- Summary.
520 $a 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.
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700 1 $a Li, Sheng. $3 1035681
700 1 $a Chu, Zhixuan. $3 3669157
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