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E-raamat: Cause Effect Pairs in Machine Learning

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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.


Arvustused

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)

Part I Fundamentals
1 The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions
3(24)
Dominik Janzing
2 Evaluation Methods of Cause-Effect Pairs
27(74)
Isabelle Guyon
Olivier Goudet
Diviyan Kalainathan
3 Learning Bivariate Functional Causal Models
101(54)
Olivier Goudet
Diviyan Kalainathan
Michele Sebag
Isabelle Guyon
4 Discriminant Learning Machines
155(36)
Diviyan Kalainathan
Olivier Goudet
Michele Sebag
Isabelle Guyon
5 Cause-Effect Pairs in Time Series with a Focus on Econometrics
191(24)
Nicolas Doremus
Alessio Moneta
Sebastiano Cattaruzzo
6 Beyond Cause-Effect Pairs
215(22)
Frederick Eberhardt
Part II Selected Readings
7 Results of the Cause-Effect Pair Challenge
237(20)
Isabelle Guyon
Alexander Statnikov
8 Non-linear Causal Inference Using Gaussianity Measures
257(44)
Daniel Hernandez-Lobato
Pablo Morales-Mombiela
David Lopez-Paz
Alberto Suarez
9 From Dependency to Causality: A Machine Learning Approach
301(20)
Gianluca Bontempi
Maxime Flauder
10 Pattern-Based Causal Feature Extraction
321(10)
Diogo Moitinho de Almeida
11 Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection
331(8)
Spyridon Samothrakis
Diego Perez
Simon Lucas
12 Conditional Distribution Variability Measures for Causality Detection
339(10)
Jose A. R. Fonollosa
13 Feature Importance in Causal Inference for Numerical and Categorical Variables
349(10)
Bram Minnaert
14 Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures
359
Eric V. Strobl
Shyam Visweswaran
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