Details
Cause Effect Pairs in Machine Learning
The Springer Series on Challenges in Machine Learning
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 22.10.2019 |
| ISBN/EAN: | 9783030218102 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
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 <i>ChaLearn Cause-Effect Pairs Challenge</i>, 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. <div><br></div><div>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.<br><div><br></div><div>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.<div><br></div><div><p></p> <p><br></p><p></p></div></div></div>
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.
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 <i>ChaLearn Cause-Effect Pairs Challenge</i>, 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. <div><br></div><div>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.<br><div><br></div><div>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.</div></div>
Comprehensive reference for those interested in the cause-effect problem, and how to tackle them using machine learning algorithms Includes six tutorial chapters, beginning with the simplest cases and common methods, to algorithmic methods that solve the cause-effect pair problem Supplemental material includes videos, slides, and code which can be found on the workshop website
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