Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method
Refereed conference paper presented and published in conference proceedings


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摘要Causal discovery for high-dimensional observations is a useful tool in many fields such as climate analysis and financial market analysis. A linear Trace method has been proposed to identify the causal direction between two linearly coupled high-dimensional observations X and Y. However, in reality, the relations between X and Y are usually nonlinear and consequently the linear Trace method may fail. In this paper, we propose a method to infer the nonlinear causal relations for two high-dimensional observations X and Y. The idea is to map the observations to high dimensional Reproducing Kernel Hilbert Space (RKHS) such that the nonlinear relations become simple linear ones. We show that the linear Trace condition holds for the causal direction but it is violated for the anti-causal direction in RKHS. Based on this theoretical result, we develop a simple algorithm to infer the causal direction for nonlinearly coupled causal pairs. Synthetic data and real world data experiments are conducted to show the effectiveness of our proposed method.
著者Chen ZT, Zhang K, Chan LW
會議名稱IEEE 13th International Conference on Data Mining (ICDM)
會議開始日07.12.2013
會議完結日10.12.2013
會議地點Dallas
會議國家/地區美國
詳細描述organized by IEEE,
出版年份2013
月份1
日期1
出版社IEEE
頁次1003 - 1008
電子國際標準書號*****************
國際標準期刊號1550-4786
語言英式英語
關鍵詞high dimensional data; kernel methods; linear Trace method; nonlinear causal discovery
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2020-16-10 於 23:57