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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Cause Effect Pairs in Machine Learning

Cause Effect Pairs in Machine Learning

Guyon, Isabelle
Statnikov, Alexander
Batu, Berna Bakir

Hardcover

Series: The Springer Challenges in Machine Learning

General ComputersProgramming

ISBN10: 3030218090
ISBN13: 9783030218096
Publisher: Springer Nature
Published: Nov 5 2019
Pages: 372
Weight: 1.59
Height: 0.88 Width: 6.14 Depth: 9.21
Language: English
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.

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