Name
Abstract | ||
|---|---|---|
In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines the concept of stability selection and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. S3C, however, is designed to model causal relations among observed variables. In this study, we extended S3C to S3C-Latent, to model linear causal relations between latent variables that are measured through observed proxies. 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 shows 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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3341557 | ACM Transactions on Intelligent Systems and Technology |
Keywords | Field | DocType |
Causal modeling,multi-objective evolutionary algorithm,specification search,stability selection,structural equation model with latent variables | Structural equation modeling,Causal relations,Latent variable,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 5 | 2157-6904 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ridho Rahmadi | 1 | 0 | 0.34 |
| Perry Groot | 2 | 175 | 17.36 |
| Tom Heskes | 3 | 1519 | 198.44 |