Machine Learning for Causal Inference

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. Cover Front Matter Part I. Introduction 1. Overview of the Book 2. Causal Inference Preliminary Part II. Machine Learning and Causal Effect Estimation 3. Causal Effect Estimation: Basic Methodologies 4. Causal Inference on Graphs 5. Causal Effect Estimation: Recent Progress, Challenges, and Opportunities Part III. Causal Inference and Trustworthy Machine Learning 6. Fair Machine Learning Through the Lens of Causality 7. Causal Explainable AI 8. Causal Domain Generalization Part IV. Applications of Causal Inference and Machine Learning 9. Causal Inference and Natural Language Processing 10. Causal Inference and Recommendations 11. Causality Encourages the Identifiability of Instance-Dependent Label Noise 12. Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data 13. Continual Causal Effect Estimation 14. Summary