Cause Effect Pairs in Machine Learning
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 classificat
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Isabelle Guyon Alexander Statnikov Berna Bakir Batu Editors
Cause Effect Pairs in Machine Learning
The Springer Series on Challenges in Machine Learning Series editors Hugo Jair Escalante, Astrofisica Optica y Electronica, INAOE, Puebla, Mexico Isabelle Guyon, ChaLearn, Berkeley, CA, USA Sergio Escalera , University of Barcelona, Barcelona, Spain
The books in this innovative series collect papers written in the context of successful competitions in machine learning. They also include analyses of the challenges, tutorial material, dataset descriptions, and pointers to data and software. Together with the websites of the challenge competitions, they offer a complete teaching toolkit and a valuable resource for engineers and scientists.
More information about this series at http://www.springer.com/series/15602
Isabelle Guyon • Alexander Statnikov Berna Bakir Batu Editors
Cause Effect Pairs in Machine Learning
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Editors Isabelle Guyon Team TAU - CNRS, INRIA Université Paris Sud Université Paris Saclay Orsay France
Alexander Statnikov SoFi San Francisco, CA, USA
ChaLearn, Berkeley CA, USA Berna Bakir Batu University of Paris-Sud Paris-Saclay, Paris, France
ISSN 2520-131X ISSN 2520-1328 (electronic) The Springer Series on Challenges in Machine Learning ISBN 978-3-030-21809-6 ISBN 978-3-030-21810-2 (eBook) https://doi.org/10.1007/978-3-030-21810-2 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
The problem of distinguishing cause from effect caught my attention, thanks to the ChaLearn Cause-Effect Pairs Challenge organized by Isabelle Guyon and her collaborators in 2013. The seminal contribution of this competition was casting the ca
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