A Robust Causal Discovery Algorithm against Faithfulness Violation
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- Isozaki Takashi
- Sony Computer Science Laboratories
抄録
Methods of statistical causal discovery that use conditional independence (CI) tests are attractive due to their time efficiency and applications to latent variable systems. However, they often suffer from worse inference results induced by statistical errors in CI tests than other approaches. We considered part of these errors to be due to statistically weak violations of a usually used assumption, called the causal faithfulness condition. We propose a causal discovery algorithm that can reduce the numbers of unnecessarily performed CI tests in this study and so provide accurate and fast inference without loss of theoretical correctness. We also introduce unreliable directions, which can reduce orientation errors caused by the locality of CI tests in the algorithm. Further, simulations are provided to demonstrate the performance of the proposed algorithm for discrete probability systems and continuous linear structural equation models.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 29 (1), 137-147, 2014
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390282680085217664
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- NII論文ID
- 130003382431
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- BIBCODE
- 2014TJSAI..29..137I
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- ISSN
- 13468030
- 13460714
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可