Practical Approaches to Causal Relationship Exploration
This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods
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Jiuyong Li • Lin Liu • Thuc Duy Le
Practical Approaches to Causal Relationship Exploration
Jiuyong Li School of Information Technology and Mathematical Sciences University of South Australia Adelaide, South Australia Australia
Thuc Duy Le School of Information Technology and Mathematical Sciences University of South Australia Adelaide, South Australia Australia
Lin Liu School of Information Technology and Mathematical Sciences University of South Australia Adelaide, South Australia Australia
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISBN 978-3-319-14432-0 ISBN 978-3-319-14433-7 (eBook) DOI 10.1007/978-3-319-14433-7 Library of Congress Control Number: 2015930835 Springer Cham Heidelberg New York Dordrecht London c The Author(s) 2015 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. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Recommended by Xuemin (Sherman) Shen.
Preface
Causal discovery aims to discover the cause-effect relationships between variables. The relationships provide explanations as to how events have happened and predictions as to which events will happen in the future. Causality has been studied and utilised in almost all disciplines, e.g. medicine, epidemiology, biology, economics, physics, social science, as a basis for explanation, prediction and decision making. Randomised controlled trials are the gold standard for discovering causal relationships. However, in many cases it is impossible to conduct randomised controlled trials due to cost, feasibility and/or ethical concerns. With the rapid explosion of data collected in various areas, it is desirable to discover causal relationships in observational data. Causal discovery in data does not only reduce the costs for many scientific explorations and assist decision making, but importantly, it also helps detect crucial signals in data
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