Estimating linear causality in the presence of latent variables
| Autor: | Youlong Yang, Nina Fei |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
Computer science Latent variable Machine learning computer.software_genre 01 natural sciences Structural equation modeling 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Econometrics 0101 mathematics Local independence Latent variable model Path analysis (statistics) Factor analysis Causal model business.industry Causality Exploratory factor analysis Latent class model Artificial intelligence business computer 030217 neurology & neurosurgery Software |
| Zdroj: | Cluster Computing. 20:1025-1033 |
| ISSN: | 1573-7543 1386-7857 |
| Popis: | Learning causality from data is known as the causal discovery problem, and it is an important and relatively new field. In many applications, there often exist latent variables, if such latent variables are completely ignored, which can lead to the estimation results seriously biased. In this paper, a method of combining exploratory factor analysis and path analysis (EFA-PA) is proposed to infer the causality in the presence of latent variables. Our method expands latent variables as well as their linear causal relationships with observed variables, which enhances the accuracy of causal models. Such model can be thought of as the simplest possible causal models for continuous data. The EFA-PA is very similar to that of structural equation model, but the theoretical model established by the structural equation model needs to be modified in the process of data fitting until the ideal model is established.The model gained by EFA-PA not only avoids subjectivity but also reduces estimation complexity. It is found that the EFA-PA estimation model is superior to the other models. EFA-PA can provides a basis for the correct estimation of the causal relationship between the observed variables in the presence of latent variables. The experiment shows that EFA-PA is better than the structural equation model. |
| Databáze: | OpenAIRE |
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