Cause Effect Pairs in Machine Learning -
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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 distri ... Full description
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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.
More Information
| Publisher | Springer Nature Switzerland |
|---|---|
| Series | The Springer Series on Challenges in Machine Learning |
| Release year | 2019 |
| Cover type | Hardcover |
| EAN | 9783030218096 |