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| Presentation | 2016-07-06 10:00 A Supervised Learning Approach to Causal Inference for Bivariate Time Series Yoichi Chikahara, Akinori Fujino (NTT)  IBISML2016-2 |
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| 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) | |
| Download PDF | IBISML2016-2 |
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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 | Author-1 |
| Date Time | 2016-07-06 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-2 |
| Volume (vol) | vol.116 |
| Number (no) | no.121 |
| Page | pp.189-194 |
| #Pages | 6 |
| Date of Issue | 2016-06-28 (IBISML) |
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