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Conditional distribution variability measures for causality detection

Author
Fonollosa, José A. R.
Type of activity
Book chapter
Book
Cause effect pairs in machine learning
First page
339
Last page
347
Publisher
Springer
Date of publication
2019
ISBN
978-3-030-21809-6 Open in new window
DOI
10.1007/978-3-030-21810-2
Repository
http://hdl.handle.net/2117/175239 Open in new window
URL
https://link.springer.com/book/10.1007/978-3-030-21810-2 Open in new window
Abstract
In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge. This book presents ground-breaking a...
Citation
Fonollosa, J. A. R. Conditional distribution variability measures for causality detection. A: "Cause effect pairs in machine learning". Berlín: Springer, 2019, p. 339-347.
Keywords
Causality detection, Cause-effect pair challenge
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants