Machine Learning for Causal Inference

Chapter 1 Overview of the Book

Chapter 2 Causal Inference Preliminary

Chapter 3 Causal Effect Estimation: Basic Methodologies

Chapter 4 Causal Inference on Graphs

Chapter 5 Causal Effect Estimation: Recent Progress, Challenges, and Opportunities

Chapter 6 Fair Machine Learning Through the Lens of Causality

Chapter 7 Causal Explainable AI

Chapter 8 Causal Domain Generalization

Chapter 9 Causal Inference and Natural Language Processing

Chapter 11 Causality Encourages the Identifiability of Instance-Dependent Label Noise

Chapter 12 Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data

Chapter 13 Continual Causal Effect Estimation

Chapter 14 Summary