Paper Abstract and Keywords |
Presentation |
2022-06-28 15:50
Causal Discovery in Discrete Data Using NML Code Length Based on MDL Principle Masatoshi Kobayashi, Nishimoto Hiroki, Shin Mastushima (Todai) NC2022-21 IBISML2022-21 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Inference on the causal structure among random variables from only a finite number of observed data is one of the most important problems in science.
This paper introduces causal inference methods for discrete variable data using NML code lengths for multinomial distribution models based on the MDL principle and BIC. These methods take an approach in which the estimation of a four-way causal relationship between two variables is directly solved as a model selection problem.
We show that this approach is an efficient and accurate causal discovery method for discrete variable pairs using synthetic data. Further, we observed that the model selection method using the NML code length can estimate causal relationships with higher accuracy. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Causal Discovery / MDL Principle / Stochastic Complexity / Discrete Data / BIC / ANMs / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 90, IBISML2022-21, pp. 149-155, June 2022. |
Paper # |
IBISML2022-21 |
Date of Issue |
2022-06-20 (NC, 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) |
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NC2022-21 IBISML2022-21 |
Conference Information |
Committee |
NC IBISML IPSJ-BIO IPSJ-MPS |
Conference Date |
2022-06-27 - 2022-06-29 |
Place (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
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Paper Information |
Registration To |
IBISML |
Conference Code |
2022-06-NC-IBISML-BIO-MPS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Causal Discovery in Discrete Data Using NML Code Length Based on MDL Principle |
Sub Title (in English) |
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Keyword(1) |
Causal Discovery |
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MDL Principle |
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Stochastic Complexity |
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Discrete Data |
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BIC |
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ANMs |
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1st Author's Name |
Masatoshi Kobayashi |
1st Author's Affiliation |
The University of Tokyo (Todai) |
2nd Author's Name |
Nishimoto Hiroki |
2nd Author's Affiliation |
The University of Tokyo (Todai) |
3rd Author's Name |
Shin Mastushima |
3rd Author's Affiliation |
The University of Tokyo (Todai) |
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Speaker |
Author-1 |
Date Time |
2022-06-28 15:50:00 |
Presentation Time |
25 minutes |
Registration for |
IBISML |
Paper # |
NC2022-21, IBISML2022-21 |
Volume (vol) |
vol.122 |
Number (no) |
no.89(NC), no.90(IBISML) |
Page |
pp.149-155 |
#Pages |
7 |
Date of Issue |
2022-06-20 (NC, IBISML) |
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