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ORCIDCause Effect Pairs in Machine Learning, 2019
- Isabelle Guyon, Alexander R. Statnikov, Berna Bakir Batu:
Cause Effect Pairs in Machine Learning. Springer 2019, ISBN 978-3-030-21809-6
Fundamentals
- Dominik Janzing:
The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions. 3-26 - Isabelle Guyon, Olivier Goudet
, Diviyan Kalainathan:
Evaluation Methods of Cause-Effect Pairs. 27-99 - Olivier Goudet, Diviyan Kalainathan, Michèle Sebag, Isabelle Guyon:
Learning Bivariate Functional Causal Models. 101-153 - Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon:
Discriminant Learning Machines. 155-189 - Nicolas Doremus, Alessio Moneta, Sebastiano Cattaruzzo:
Cause-Effect Pairs in Time Series with a Focus on Econometrics. 191-214 - Frederick Eberhardt:
Beyond Cause-Effect Pairs. 215-233
Selected Readings
- Isabelle Guyon, Alexander R. Statnikov:
Results of the Cause-Effect Pair Challenge. 237-256 - Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference Using Gaussianity Measures. 257-299 - Gianluca Bontempi, Maxime Flauder:
From Dependency to Causality: A Machine Learning Approach. 301-320 - Diogo Moitinho de Almeida:
Pattern-Based Causal Feature Extraction. 321-329 - Spyridon Samothrakis, Diego Perez Liebana, Simon M. Lucas:
Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection. 331-338 - José A. R. Fonollosa:
Conditional Distribution Variability Measures for Causality Detection. 339-347 - Bram Minnaert:
Feature Importance in Causal Inference for Numerical and Categorical Variables. 349-358 - Eric V. Strobl, Shyam Visweswaran:
Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures. 359-372
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