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Cause Effect Pairs in Machine Learning

Overview of attention for book
Cover of 'Cause Effect Pairs in Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions
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    Chapter 2 Evaluation Methods of Cause-Effect Pairs
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    Chapter 3 Learning Bivariate Functional Causal Models
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    Chapter 4 Discriminant Learning Machines
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    Chapter 5 Cause-Effect Pairs in Time Series with a Focus on Econometrics
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    Chapter 6 Beyond Cause-Effect Pairs
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    Chapter 7 Results of the Cause-Effect Pair Challenge
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    Chapter 8 Non-linear Causal Inference Using Gaussianity Measures
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    Chapter 9 From Dependency to Causality: A Machine Learning Approach
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    Chapter 10 Pattern-Based Causal Feature Extraction
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    Chapter 11 Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection
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    Chapter 12 Conditional Distribution Variability Measures for Causality Detection
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    Chapter 13 Feature Importance in Causal Inference for Numerical and Categorical Variables
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    Chapter 14 Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures
Attention for Chapter 11: Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection
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7 Mendeley
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Chapter title
Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection
Chapter number 11
Book title
Cause Effect Pairs in Machine Learning
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-21810-2_11
Book ISBNs
978-3-03-021809-6, 978-3-03-021810-2
Authors

Spyridon Samothrakis, Diego Perez, Simon Lucas

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Researcher 2 29%
Student > Master 2 29%
Student > Doctoral Student 1 14%
Readers by discipline Count As %
Computer Science 4 57%
Mathematics 2 29%
Engineering 1 14%

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