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Hokkaido University Collection of Scholarly and Academic Papers >
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Identification of Possible Common Causes by Intrinsic Dimension Estimation
| 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)
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Submitter: 小山 聡
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