Name
Abstract | ||
|---|---|---|
Latent variables represent unmeasured causal factors. Some, such as intelligence, cannot be directly measured; others may be, but we do not know about them or know how to measure them when making our observations. Regardless, in many cases, the influence of latent variables is real and important, and optimal modeling cannot be done without them. However, in many of those cases the influence of latent variables reveals itself in patterns of measured dependency that cannot be reproduced using the observed variables alone, under the assumptions of the causal Markov property and faithfulness. In such cases, latent variables may be posited to the advantage of the causal discovery process. All latent variable discovery takes advantage of this; we make the process explicit. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-51691-2_12 | ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE, ACALCI 2017 |
Keywords | DocType | Volume |
Bayesian networks, Causal discovery, Latent variables | Conference | 10142 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuhui Zhang | 1 | 0 | 0.34 |
| Kevin B. Korb | 2 | 400 | 52.03 |
| Ann E. Nicholson | 3 | 692 | 88.01 |
| Steven Mascaro | 4 | 0 | 0.34 |