Causal discovery in the presence of hidden confounding variables for data with heterogeneity
Project/Area Number |
16K00045
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Statistical science
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Research Institution | Shiga University |
Principal Investigator |
Shimizu Shohei 滋賀大学, データサイエンス学部, 教授 (10509871)
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Project Period (FY) |
2016-04-01 – 2020-03-31
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Project Status |
Completed (Fiscal Year 2019)
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Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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Keywords | 因果探索 / 因果構造 / 観察データ / 未観測共通原因 / 異質性 / 統計的因果推論 / 構造的因果モデル / 因果構造探索 |
Outline of Final Research Achievements |
LiNGAM model handles only continuous variables. To represent heterogeneity, we tried to extend the LiNGAM model so that it can handle discrete variables. We developed a model assuming that the relationship between discrete variables and continuous variables is a non-cyclic directed graph. We also considered combining a causal model with a machine learning model that can handle discrete variables. To deal with unobserved common causes, we extended instrumental variable methods by making use of non-Gaussianity and independence. In addition, a method to infer where the unobserved common cause is likely to be is proposed within the framework of the LiNGAM model.
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Academic Significance and Societal Importance of the Research Achievements |
LiNGAMモデルは因果探索の標準的な方法の一つとして注目を集めているが, 離散変数が混在する状況を扱えるようにすることでさらに応用範囲を広げることができた。また機械学習モデルと因果モデルを組み合わせたモデルについては,制御への応用が期待される。操作変数は広く用いられているが,非ガウス性と独立性を利用した操作変数法については,従来よりも多くの情報を抽出することができることがわかった。未観測共通原因がどこにありそうかを推測する方法については, 条件付き独立性を用いる因果関係推測法の枠組みでは, そのような方法が提案されているが, LiNGAMモデルの枠組みでは対応する方法がなかった。
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Report
(5 results)
Research Products
(35 results)
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[Journal Article] Cause-Effect Inference by Comparing Regression Errors2018
Author(s)
Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schoelkopf
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Journal Title
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS2018), PMLR
Volume: 84
Pages: 900-909
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Presentation] 因果構造探索の基本2017
Author(s)
清水昌平
Organizer
研究集会: 因果推論の基礎
Place of Presentation
統計数理研究所 (東京)
Year and Date
2017-02-17
Related Report
Invited
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[Book] 統計的因果探索2017
Author(s)
清水 昌平
Total Pages
192
Publisher
講談社
ISBN
9784061529250
Related Report
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