A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
This work addresses a specific methodological limitation in causal inference for researchers dealing with non-Gaussian observational data, representing an incremental improvement.
The paper tackles the problem of biased estimation in linear non-Gaussian acyclic models (LiNGAM) when latent classes are present, proposing a new Bayesian estimation procedure to address this issue.
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. A linear non-Gaussian acyclic model (LiNGAM) based on the non-Gaussianity of external influences has been proposed to estimate the data-generating processes of variables. However, the results of the estimation can be biased if there are latent classes. In this paper, we first review LiNGAM, its extended model, as well as the estimation procedure for LiNGAM in a Bayesian framework. We then propose a new Bayesian estimation procedure that solves the problem.