Description 
1 online resource (357 pages) : illustrations (black and white, and color) 
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 InstanceDependent Label Noise  Causal Interventional Time Series Forecasting on Multihorizon and Multiseries Data  Continual Causal Effect Estimation  Summary 
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, treebased 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, nondiscrimination 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 machinelearning models from data 
Notes 
Description based on online resource; title from digital title page (viewed on December 13, 2023) 
Subject 
Machine learning.


Inference  Data processing


Causation  Data processing


Inference  Data processing.


Machine learning.

Form 
Electronic book

Author 
Li, Sheng, editor


Chu, Zhixuan, editor

ISBN 
9783031350511 

3031350510 
