FOM: Fourth-order moment based causal direction identification on the heteroscedastic data
| Autor: | Huiyuan Fu, Jincheng Ye, Zhifeng Hao, Jie Qiao, Ruichu Cai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Heteroscedasticity Statistical assumption Computer science Cognitive Neuroscience Normal Distribution 02 engineering and technology Signal-To-Noise Ratio Moment (mathematics) Noise Variable (computer science) Identification (information) 020901 industrial engineering & automation Artificial Intelligence Kriging 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Algorithms Independence (probability theory) |
| Zdroj: | Neural Networks. 124:193-201 |
| ISSN: | 0893-6080 |
| DOI: | 10.1016/j.neunet.2020.01.006 |
| Popis: | Identification of the causal direction is a fundamental problem in many scientific research areas. The independence between the noise and the cause variable is a widely used assumption to identify the causal direction. However, such an independence assumption is usually violated due to heteroscedasticity of the real-world data. In this paper, we propose a new criterion for the causal direction identification which is robust to the heteroscedasticity of the data. In detail, the fourth-order moment of noise is proposed to measure the asymmetry between the cause and effect. A heteroscedastic Gaussian process regression-based estimation of the fourth-order moment is proposed accordingly. Under some commonly used assumptions of the causal mechanism, we theoretically show that the noise’s fourth-order moment of the causal direction is smaller than that of the anti-causal direction. Experimental results on both simulated and real-world data illustrate the efficiency of the proposed approach. |
| Databáze: | OpenAIRE |
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