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Paper Abstract and Keywords
Presentation 2016-07-06 10:00
A Supervised Learning Approach to Causal Inference for Bivariate Time Series
Yoichi Chikahara, Akinori Fujino (NTT) IBISML2016-2
Abstract (in Japanese) (See Japanese page) 
(in English) Causal inference in time series is a problem to estimate the underlying causal relationship between time-dependent variables. In this report, we tackle two tasks relating to causal inference in bivariate time series; one is to estimate the causal direction ($X rightarrow Y$ or $X leftarrow Y$) and the other is to detect latent confounding ($X leftarrow C rightarrow Y$). By extending the Random Causation Coefficient (RCC) to time series, we propose a supervised learning framework for bivariate time series, which infers the causal relationship based on a set of time series with a known causal relationship. While the existing model-based approaches require the prior specification of the models representing the causal relationships, the supervised learning framework can give the definition of the causal relationships directly from data. We show experimentally that our proposed method achieves better causal inference accuracy than other existing methods and even can detect latent confounding.
Keyword (in Japanese) (See Japanese page) 
(in English) causal inference / time series analysis / kernel embedding / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 121, IBISML2016-2, pp. 189-194, July 2016.
Paper # IBISML2016-2 
Date of Issue 2016-06-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2016-07-04 - 2016-07-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2016-07-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Supervised Learning Approach to Causal Inference for Bivariate Time Series 
Sub Title (in English)  
Keyword(1) causal inference  
Keyword(2) time series analysis  
Keyword(3) kernel embedding  
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1st Author's Name Yoichi Chikahara  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Akinori Fujino  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker
Date Time 2016-07-06 10:00:00 
Presentation Time 25 
Registration for IBISML 
Paper # IEICE-IBISML2016-2 
Volume (vol) IEICE-116 
Number (no) no.121 
Page pp.189-194 
#Pages IEICE-6 
Date of Issue IEICE-IBISML-2016-06-28 


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