Cause Effect Pairs in Machine Learning

1. Front Matter

   Pages i-xvi

   PDF

2. Fundamentals

   1. Front Matter

      Pages 1-1

      PDF

   2. The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions

      • Dominik Janzing

      Pages 3-26

   3. Evaluation Methods of Cause-Effect Pairs

      • Isabelle Guyon, Olivier Goudet, Diviyan Kalainathan

      Pages 27-99

   4. Learning Bivariate Functional Causal Models

      • Olivier Goudet, Diviyan Kalainathan, Michèle Sebag, Isabelle Guyon

      Pages 101-153

   5. Discriminant Learning Machines

      • Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon

      Pages 155-189

   6. Cause-Effect Pairs in Time Series with a Focus on Econometrics

      • Nicolas Doremus, Alessio Moneta, Sebastiano Cattaruzzo

      Pages 191-214

   7. Beyond Cause-Effect Pairs

      • Frederick Eberhardt

      Pages 215-233

3. Selected Readings

   1. Front Matter

      Pages 235-235

      PDF

   2. Results of the Cause-Effect Pair Challenge

      • Isabelle Guyon, Alexander Statnikov

      Pages 237-256

   3. Non-linear Causal Inference Using Gaussianity Measures

      • Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

      Pages 257-299

   4. From Dependency to Causality: A Machine Learning Approach

      • Gianluca Bontempi, Maxime Flauder

      Pages 301-320

   5. Pattern-Based Causal Feature Extraction

      • Diogo Moitinho de Almeida

      Pages 321-329

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

      • Spyridon Samothrakis, Diego Perez, Simon Lucas

      Pages 331-338

   7. Conditional Distribution Variability Measures for Causality Detection

      • Josè A.  R. Fonollosa

      Pages 339-347

   8. Feature Importance in Causal Inference for Numerical and Categorical Variables

      • Bram Minnaert

      Pages 349-358

   9. Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures

      • Eric V. Strobl, Shyam Visweswaran

      Pages 359-372

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms.  Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.  

This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.


Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

• Causality
• cause-effect pairs
• large scale design
• causal direction
• causal inference
• causality in machine learning
• causal graphs
• causal structure learning
• causal mechanisms

“The book can be recommended for researchers in causal discovery with expertise in either statistics or machine learning. Although the chapters are written by different authors, readers will appreciate the book's coherent organization ... . ” (Corrado Mencar, Computing Reviews, May 17, 2022)

• Team TAU - CNRS, INRIA, Université Paris Sud, Université Paris Saclay, Orsay France, ChaLearn, Berkeley, USA

  Isabelle Guyon

• SoFi, San Francisco, USA

  Alexander Statnikov

• University of Paris-Sud, Paris-Saclay, France

  Berna Bakir Batu

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