Machine Learning for Causal Inference

✍ Scribed by Sheng Li; Zhixuan Chu

Publisher: Springer International Publishing
Year: 2023
Tongue: English
Leaves: 544
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

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

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