Cause Effect Pairs in Machine Learning

Description

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. Front Matter ....Pages i-xvi Front Matter ....Pages 1-1 The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions (Dominik Janzing)....Pages 3-26 Evaluation Methods of Cause-Effect Pairs (Isabelle Guyon, Olivier Goudet, Diviyan Kalainathan)....Pages 27-99 Learning Bivariate Functional Causal Models (Olivier Goudet, Diviyan Kalainathan, Michèle Sebag, Isabelle Guyon)....Pages 101-153 Discriminant Learning Machines (Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon)....Pages 155-189 Cause-Effect Pairs in Time Series with a Focus on Econometrics (Nicolas Doremus, Alessio Moneta, Sebastiano Cattaruzzo)....Pages 191-214 Beyond Cause-Effect Pairs (Frederick Eberhardt)....Pages 215-233 Front Matter ....Pages 235-235 Results of the Cause-Effect Pair Challenge (Isabelle Guyon, Alexander Statnikov)....Pages 237-256 Non-linear Causal Inference Using Gaussianity Measures (Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez)....Pages 257-299 From Dependency to Causality: A Machine Learning Approach (Gianluca Bontempi, Maxime Flauder)....Pages 301-320 Pattern-Based Causal Feature Extraction (Diogo Moitinho de Almeida)....Pages 321-329 Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection (Spyridon Samothrakis, Diego Perez, Simon Lucas)....Pages 331-338 Conditional Distribution Variability Measures for Causality Detection (Josè A. R. Fonollosa)....Pages 339-347 Feature Importance in Causal Inference for Numerical and Categorical Variables (Bram Minnaert)....Pages 349-358 Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures (Eric V. Strobl, Shyam Visweswaran)....Pages 359-372