Inferring causal directions from uncertain data. (October 2017)

• Record Type: Journal Article
• Authors: Zhang, Yulai; Ma, Weifeng; Luo, Guiming
• Is Part Of: Engineering applications of artificial intelligence. Volume 65(2017:May)
• Page Start: 261
• Page End: 267
• Publication Date: 2017-10
• Subjects: Causal direction -- Errors-in-variables model -- EM algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.05.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Causal knowledge discovery is an essential task in many disciplines. Inferring the knowledge of causal directions from the measurement data of two correlated variables is one of the most basic but non-trivial problems in the research of causal discovery. Most of the existing methods assume that at least one of the variables is strictly measured. In practice, uncertain data with observation error is widely exists and is unavoidable for both the cause and the effect. Correct causal relationships will be blurred by such noise. A causal direction inference method based on the errors-in-variables (EIV) model is proposed in this work. All variables are assumed to be measured with observation errors in the errors-in-variables models. Causal directions will be inferred by computing the correlation coefficients between the regression model functions and the probability density functions on both of the possible causal directions. Experiments are done on artificial data sets and the real world data sets to illustrate the performance of the proposed method.