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Machine Learning for Causal Inference

Produktform: E-Buch Text Elektronisches Buch in proprietärem

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. weiterlesen

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-3-031-35051-1 / 978-3031350511 / 9783031350511

Verlag: Springer International Publishing

Erscheinungsdatum: 25.11.2023

Seiten: 298

Herausgegeben von Sheng Li, Zhixuan Chu

149,79 € inkl. MwSt.
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