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| Presentation | 2018-11-05 15:10 [Poster Presentation] A Note on the Estimation Method of Causality Effects based on Statistical Decision Theory Shunsuke Horii, Tota Suko (Waseda Univ.)  IBISML2018-97 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we deal with the problem of estimating the intervention effect in statistical causal analysis using structural equation model and causal diagram. The intervention effect is defined as causal effect on the response variable $Y$ when the causal variable $X$ is fixed to a certain value by external operation and is defined based on the causal diagram. The intervention effect is defined as a function of the probability distributions in the causal diagram, however, generally these probability distributions are unknown, so it is required to estimate them from data. In other words, the steps of the estimation of the intervention effect using the causal diagram are as follows: 1. Estimate the causal diagram from the data, 2. Estimate the probability distributions in the causal diagram from the data, 3. Calculate the intervention effect. However, if the problem of estimating the intervention effect is formulated in the statistical decision theory framework, estimation with this procedure is not necessarily optimal. In this study, we formulate the problem of estimating the intervention effect for the two cases, the case where the causal diagram is known and the case where it is unknown, in the framework of statistical decision theory and derive the optimal decision method under the Bayesian criterion. We show the effectiveness of the proposed method through numerical simulation. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Statistical causal inference / Causal diagram / Intervention effect / Statistical decision theory / Bayesian decision theory / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 284, IBISML2018-97, pp. 397-402, Nov. 2018. | |
| Paper # | IBISML2018-97 | |
| Date of Issue | 2018-10-29 (IBISML) | |
| ISSN | 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 | IBISML2018-97 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2018-11-05 - 2018-11-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Citizens Activites Center (Kaderu 2.7) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2018) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Note on the Estimation Method of Causality Effects based on Statistical Decision Theory |
| Sub Title (in English) | |
| Keyword(1) | Statistical causal inference |
| Keyword(2) | Causal diagram |
| Keyword(3) | Intervention effect |
| Keyword(4) | Statistical decision theory |
| Keyword(5) | Bayesian decision theory |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shunsuke Horii |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Tota Suko |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-11-05 15:10:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2018-97 |
| Volume (vol) | vol.118 |
| Number (no) | no.284 |
| Page | pp.397-402 |
| #Pages | 6 |
| Date of Issue | 2018-10-29 (IBISML) |
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