Inferring causal directions from uncertain data. (October 2017)
- Record Type:
- Journal Article
- Title:
- Inferring causal directions from uncertain data. (October 2017)
- Main Title:
- Inferring causal directions from uncertain data
- Authors:
- Zhang, Yulai
Ma, Weifeng
Luo, Guiming - 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.
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 65(2017:May)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 65(2017:May)
- Issue Display:
- Volume 65 (2017)
- Year:
- 2017
- Volume:
- 65
- Issue Sort Value:
- 2017-0065-0000-0000
- 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 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2017.05.007 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4714.xml