Cause Effect Pairs in Machine Learning

  • Isabelle Guyon
  • Alexander Statnikov
  • Berna Bakir Batu

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Isabelle Guyon, Olivier Goudet, Diviyan Kalainathan
      Pages 27-99
    3. Olivier Goudet, Diviyan Kalainathan, Michèle Sebag, Isabelle Guyon
      Pages 101-153
    4. Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon
      Pages 155-189
    5. Nicolas Doremus, Alessio Moneta, Sebastiano Cattaruzzo
      Pages 191-214
    6. Frederick Eberhardt
      Pages 215-233
  3. Selected Readings

    1. Front Matter
      Pages 235-235
    2. Isabelle Guyon, Alexander Statnikov
      Pages 237-256
    3. Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
      Pages 257-299
    4. Gianluca Bontempi, Maxime Flauder
      Pages 301-320
    5. Diogo Moitinho de Almeida
      Pages 321-329
    6. Eric V. Strobl, Shyam Visweswaran
      Pages 359-372

About this book


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

Editors and affiliations

  • Isabelle Guyon
    • 1
  • Alexander Statnikov
    • 2
  • Berna Bakir Batu
    • 3
  1. 1.Team TAU - CNRS, INRIA, Université Paris Sud, Université Paris Saclay, Orsay FranceChaLearnBerkeleyUSA
  2. 2.SoFiSan FranciscoUSA
  3. 3.University of Paris-SudParis-SaclayFrance

Bibliographic information

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences