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Machine Learning for Causal Inference (1st ed. 2023)
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Machine Learning for Causal Inference (1st ed. 2023)
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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.
General
Imprint: |
Springer International Publishing AG
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Country of origin: |
Switzerland |
Release date: |
November 2023 |
First published: |
2023 |
Editors: |
Sheng Li
• Zhixuan Chu
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Dimensions: |
235 x 155mm (L x W) |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-135050-4 |
Categories: |
Books
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LSN: |
3-03-135050-2 |
Barcode: |
9783031350504 |
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