Stable specification search in structural equation model with latent variables
This work addresses causal discovery with latent variables, which is important for fields like psychology and education, but it is incremental as it builds directly on prior research.
The authors extended a stable specification search method to model causal relations between latent variables (S3C-Latent), achieving better performance than a state-of-the-art method on simulated data and consistent results on real-world datasets.
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.