| Presentation | 2016-07-06 A Supervised Learning Approach to Causal Inference for Bivariate Time Series Yoichi Chikahara, Akinori Fujino, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | causal inference / time series analysis / kernel embedding |
| Paper # | IBISML2016-2 |
| Date of Issue | 2016-06-28 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2016/7/4(3days) |
| 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 |
| Chair | Shigeo Sato(Tohoku Univ.) / / Kenji Fukumizu(ISM) |
| Vice Chair | Masafumi Hagiwara(Keio Univ.) / / Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Masafumi Hagiwara(Kyoto Sangyo Univ.) / (Tokyo Inst. of Tech.) / Masashi Sugiyama / Hisashi Kashima(Univ. of Tokyo) / (Nagoya Inst. of Tech.) |
| Assistant | Hisanao Akima(Tohoku Univ.) / Yoshihisa Shinozawa(Keio Univ.) / / Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Special Interest Group on Bioinformatics and Genomics / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | JPN |
| 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 |
| 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) |
| Date | 2016-07-06 |
| Paper # | IBISML2016-2 |
| Volume (vol) | vol.116 |
| Number (no) | IBISML-121 |
| Page | pp.pp.189-194(IBISML), |
| #Pages | 6 |
| Date of Issue | 2016-06-28 (IBISML) |