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Cause Effect Pairs in Machine Learning

  • Book
  • © 2019

Overview

Editors:
  1. Isabelle Guyon

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

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    You can also search for this editor in PubMed   Google Scholar

  2. Alexander Statnikov

    1. SoFi, San Francisco, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Berna Bakir Batu

    1. University of Paris-Sud, Paris-Saclay, France

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    You can also search for this editor in PubMed   Google Scholar

  • Comprehensive reference for those interested in the cause-effect problem, and how to tackle them using machine learning algorithms
  • Includes six tutorial chapters, beginning with the simplest cases and common methods, to algorithmic methods that solve the cause-effect pair problem
  • Supplemental material includes videos, slides, and code which can be found on the workshop website

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Fundamentals

    1. Front Matter

      Pages 1-1
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    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
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    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
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Keywords

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.





Reviews

“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)

Editors and Affiliations

  • 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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