Chapter 1 The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions

Chapter 2 Evaluation Methods of Cause-Effect Pairs

Chapter 3 Learning Bivariate Functional Causal Models

Chapter 4 Discriminant Learning Machines

Chapter 5 Cause-Effect Pairs in Time Series with a Focus on Econometrics

Chapter 6 Beyond Cause-Effect Pairs

Chapter 7 Results of the Cause-Effect Pair Challenge

Chapter 8 Non-linear Causal Inference Using Gaussianity Measures

Chapter 9 From Dependency to Causality: A Machine Learning Approach

Chapter 10 Pattern-Based Causal Feature Extraction

Chapter 11 Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection

Chapter 13 Feature Importance in Causal Inference for Numerical and Categorical Variables

Chapter 14 Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures