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Identification of Possible Common Causes by Intrinsic Dimension Estimation

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/76910

Title: Identification of Possible Common Causes by Intrinsic Dimension Estimation
Authors: Song, Jing Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: common cause identification
ntrinsic dimension
conditional independence test
Issue Date: 2019
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
Start Page: 1
End Page: 8
Publisher DOI: 10.1109/BIGCOMP.2019.8679343
Abstract: The effect of confounding factors cannot be ignored in real world causal discovery tasks. A common cause is a general confounder between two variables. In this paper, we propose using intrinsic dimension estimation as a necessary condition to determine a possible common cause for two variables. Simulated application showed that the proposed method worked well for both linear and non-linear functions. Testing using different types of noise showed that it generally worked well for different types of added noise. In particular, it worked better than a kernel-based conditional independence test for Poisson noise. Testing of how the estimated intrinsic dimension is affected by different types of distributions showed that the estimated dimension is nearly not affected by the type of distribution. Simulation of mixed pattern showed that the proposed method can still tell a possible common cause when it is mixed with causal relationship. Finally, experiments using variables from the CauseEffectPairs dataset showed that the proposed method can give correct inferred results for real world data.
Description: 2019 IEEE International Conference will be held 27 Feb.-2 March 2019 at Kyoto, Japan,
Conference Name: 2019 IEEE International Conference
Conference Place: Kyoto
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/76910
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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