Machine learning for causal inference / Sheng Li, Zhixuan Chu, editors.
2023
Q325.5 .M33 2023
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Details
Title
Machine learning for causal inference / Sheng Li, Zhixuan Chu, editors.
ISBN
9783031350511 electronic book
3031350510 electronic book
3031350502
9783031350504
3031350510 electronic book
3031350502
9783031350504
Published
Cham, Switzerland : Springer, [2023]
Language
English
Description
1 online resource (357 pages) : illustrations (black and white, and color).
Other Standard Identifiers
10.1007/978-3-031-35051-1 doi
Call Number
Q325.5 .M33 2023
Dewey Decimal Classification
006.31
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.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on December 13, 2023).
Added Author
Li, Sheng, editor. 1828-1906
Chu, Zhixuan, editor.
Chu, Zhixuan, editor.
Available in Other Form
MACHINE LEARNING FOR CAUSAL INFERENCE.
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Table of Contents
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.
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.