In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for Latent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results.
The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.
Most existing causal discovery methods either ignore the discrete data and apply a continuous-valued algorithm or discretize all the continuous data and then apply a discrete Bayesian network approach.
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated.
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions.
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables.
A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data.
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.
We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables.
In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders.