| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
There is an increasing need to understand the mechanism of large-scale systems by analyzing big data by statistical causal inference. However, its practical principles and methods have been established only for large-scale systems that are linear and have non-Gaussian noise. This research achieved (1) establishment of a new principle for estimating the causal relationship between many observation variables in a non-linear system with high accuracy, (2) development of statistical causal inference methods for large-scale systems by further extending the new principle, (3) basic performance verification using large-scale artificial data, and (4) practical performance verification using real-world data. We developed practical principles and methods for a wide range of large-scale nonlinear systems though these studies. Furthermore, we presented these results in major international conferences and international journals, and spread the breakthrough method of statistical causal reasoning.
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