Estimation of high-dimensional causal networks based on multiple datasets and its applications to biomedical science
| Project/Area Number |
24700275
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | Osaka University |
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Principal Investigator |
Shimizu Shohei 大阪大学, 産業科学研究所, 准教授 (10509871)
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| Project Period (FY) |
2012-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2013: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2012: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 因果探索 / 非ガウス性 / 構造方程式モデル / 複数データセット / 因果構造探索 / 国際情報交換 / 米国 / 潜在共通原因 / 因果推論 / 因果方向推定 / 非ガウス / LiNGAM |
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| Outline of Final Research Achievements |
We developed an estimation principle and estimation algorithms to estimate LiNGAM models from multiple datasets for causal structure learning. Moreover, we extended them to cases with latent confounding variables, latent classes, temporally changing causal structures. The latent class extension can be seen as a nonlinear extension. We applied those methods to brain imaging data.
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Report
(5 results)
Research Products
(34 results)
