Cause Effect Pairs in Machine Learning / edited by Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu.

Format:

Book

Contributor:

Guyon, Isabelle, editor.

Statnikov, Alexander, editor.

Batu, Berna Bakir, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Springer series on challenges in machine learning 2520-131X

The Springer Series on Challenges in Machine Learning, 2520-131X

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Pattern perception.

Artificial Intelligence.

Image Processing and Computer Vision.

Pattern Recognition.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Pattern Recognition.

Physical Description:

1 online resource (XVI, 372 pages) : 122 illustrations, 90 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

1. The cause-effect problem: motivation, ideas, and popular misconceptions

2. Evaluation methods of cause-effect pairs

3. Learning Bivariate Functional Causal Models

4. Discriminant Learning Machines

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

6. Beyond cause-effect pairs

7. Results of the Cause-Effect Pair Challenge

8. Non-linear Causal Inference using Gaussianity Measures

9. From Dependency to Causality: A Machine Learning Approach

10. Pattern-based Causal Feature Extraction

11. Training Gradient Boosting Machines using Curve-fitting and Information-theoretic Features for Causal Direction Detection

12. Conditional distribution variability measures for causality detection

13. Feature importance in causal inference for numerical and categorical variables

14. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.

Other Format:

Printed edition:

ISBN:

978-3-030-21810-2

9783030218102

9783030218096

9783030218119

9783030218126

Access Restriction:

Restricted for use by site license.