  In our previous study, we introduced stable specification search for
cross-sectional data (S3C). It is an exploratory causal method that combines
stability selection concept and multi-objective optimization to search for
stable and parsimonious causal structures across the entire range of model
complexities. In this study, we extended S3C to S3C-Latent, to model causal
relations between latent variables. We evaluated S3C-Latent on simulated data
and compared the results to those of PC-MIMBuild, an extension of the PC
algorithm, the state-of-the-art causal discovery method. The comparison showed
that S3C-Latent achieved better performance. We also applied S3C-Latent to
real-world data of children with attention deficit/hyperactivity disorder and
data about measuring mental abilities among pupils. The results are consistent
with those of previous studies.
