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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
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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 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) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
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)  
Keyword(1) Causal Discovery  
Keyword(2) MDL Principle  
Keyword(3) Stochastic Complexity  
Keyword(4) Discrete Data  
Keyword(5) BIC  
Keyword(6) ANMs  
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Keyword(8)  
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
Date of Issue 2022-06-20 (NC, IBISML) 


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