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  • © 2023

Machine Learning for Causal Inference

  • 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 methods and improves performance

  • Comprehensive survey by contributors who have applied causal inference techniques in various business scenarios

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xvi
  2. Introduction

    1. Front Matter

      Pages 1-1
    2. Overview of the Book

      • Zhixuan Chu, Sheng Li
      Pages 3-5
    3. Causal Inference Preliminary

      • Liuyi Yao, Zhixuan Chu, Yaliang Li, Jing Gao, Aidong Zhang, Sheng Li
      Pages 7-19
  3. Machine Learning and Causal Effect Estimation

    1. Front Matter

      Pages 21-21
    2. Causal Effect Estimation: Basic Methodologies

      • Liuyi Yao, Zhixuan Chu, Yaliang Li, Jing Gao, Aidong Zhang, Sheng Li
      Pages 23-52
    3. Causal Inference on Graphs

      • Jing Ma, Ruocheng Guo, Jundong Li
      Pages 53-78
  4. Causal Inference and Trustworthy Machine Learning

    1. Front Matter

      Pages 101-101
    2. Fair Machine Learning Through the Lens of Causality

      • Yongkai Wu, Lu Zhang, Xintao Wu
      Pages 103-135
    3. Causal Explainable AI

      • Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang
      Pages 137-159
    4. Causal Domain Generalization

      • Paras Sheth, Huan Liu
      Pages 161-185
  5. Applications of Causal Inference and Machine Learning

    1. Front Matter

      Pages 187-187
    2. Causal Inference and Natural Language Processing

      • Wenqing Chen, Zhixuan Chu
      Pages 189-206
    3. Causal Inference and Recommendations

      • Yaochen Zhu, Jing Ma, Jundong Li
      Pages 207-245
    4. Causality Encourages the Identifiability of Instance-Dependent Label Noise

      • Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
      Pages 247-264
    5. Continual Causal Effect Estimation

      • Zhixuan Chu, Stephen L. Rathbun, Sheng Li
      Pages 283-295
    6. Summary

      • Sheng Li, Zhixuan Chu
      Pages 297-298

About this book

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.

Editors and Affiliations

  • University of Virginia, Charlottesville, USA

    Sheng Li

  • Ant Group, Hangzhou, China

    Zhixuan Chu

About the editors

Dr. Sheng Li is an Assistant Professor of Data Science at the University of Virginia (UVA). Prior to joining UVA, he was an Assistant Professor of Computer Science at the University of Georgia (UGA) from 2018 to 2022, and was a Data Scientist at Adobe Research from 2017 to 2018. He obtained his Ph.D. degree in computer engineering from Northeastern University in 2017. Dr. Li is broadly interested in the areas of data science, machine learning, artificial intelligence, and their interdisciplinary applications. His recent research interests include trustworthy representation learning and causal inference. He has published over 140 papers and has received over 10 research awards, such as the INNS Young Investigator Award, Fred C. Davidson Early Career Scholar Award, Adobe Data Science Research Award, Cisco Faculty Research Award, and SDM Best Paper Award. He has served as Associate Editor for seven journals such as IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Circuits and Systems for Video Technology, and as an Area Chair for NeurIPS, ICLR, ICML and IJCAI.


Dr. Zhixuan Chu is a researcher at Ant Group. He holds a Ph.D. in Biostatistics and a Master's degree in Computer Science from the University of Georgia, along with a Bachelor's degree in Statistics from Huazhong University of Science and Technology. His research pursuits are centered around trustworthy artificial intelligence and the various interdisciplinary applications it offers, with a particular focus on causal inference, striving to improve the effectiveness of causal inference with machine learning technologies and enhance the stability and interpretability of machine learning with causal inference technologies. He has published over 20 papers in top-tier computer science conferences and journals.

Bibliographic Information

Buy it now

Buying options

Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access