| 书目名称 | Machine Learning for Causal Inference | | 编辑 | Sheng Li,Zhixuan Chu | | 视频video | | | 概述 | Reviews novel causal inference methods with the help of machine learning to solve problems in a wide variety of fields.Addresses robustness and interpretability challenges posed by conventional ML met | | 图书封面 |  | | 描述 | 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 m | | 出版日期 | Book 2023 | | 关键词 | Causality; Counterfactuals; Treatment Effect Estimation; Causal Discovery; statistics | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-35051-1 | | isbn_softcover | 978-3-031-35053-5 | | isbn_ebook | 978-3-031-35051-1 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|
发表于 2025-3-21 18:14:13
