Cause Effect Pairs in Machine Learning

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

Chapter 12 Conditional Distribution Variability Measures for Causality Detection

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

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