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Reverse engineering for causal discovery based on monotonic characteristic of causal structure

Authors
Ko, SongLim, HyunkiKim, Dae-Won
Issue Date
Aug-2017
Publisher
ELSEVIER SCIENCE BV
Keywords
Bayesian networks; Causal relation; Structure learning; Heuristic algorithm; Bilateral consistency
Citation
PATTERN RECOGNITION LETTERS, v.95, pp 91 - 97
Pages
7
Journal Title
PATTERN RECOGNITION LETTERS
Volume
95
Start Page
91
End Page
97
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4101
DOI
10.1016/j.patrec.2017.06.014
ISSN
0167-8655
1872-7344
Abstract
Bayesian networks provide a useful tool for causal reasoning among random variables (nodes) in fields. However, a critical limitation is that it is extremely difficult to obtain an effective causal structure from datasets. In-depth research is required to design a more sophisticated learning algorithm, despite various such algorithms having been introduced to date. In this paper, we present a novel learning algorithm based on the monotonic characteristic of causal relations among a subset of nodes. For example, the magnitude of the causality (dependency) between a child node and its parent node is greater than that between a child node and its grandparent node. Therefore, a child node obtains a higher causality score under its parent node than its grandparent node. We identified the monotonic characteristic in various datasets and designed the proposed method in order to infer causal relations based on the monotonic characteristic. Our experimental results demonstrate that the proposed method significantly improves performance compared to previous methods. (C) 2017 Elsevier B.V. All rights reserved.
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소프트웨어대학 (소프트웨어학부)
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