Name
Abstract | ||
|---|---|---|
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.5555/1248547.1248555 | Journal of Machine Learning Research |
Keywords | Field | DocType |
markov assumption,causality,causal relation,brief study,latent variable models,faithfulness assumption,recorded variable,unrecorded variable,single common unrecorded cause,graphical models,acyclic graph,disjoint subsets,latent factor,linear latent variable models,social science,latent variable model,graphical model,satisfiability,sample size,directed acyclic graph | Combinatorics,Disjoint sets,Markov property,Latent variable model,Latent class model,Directed acyclic graph,Latent variable,Artificial intelligence,Local independence,Graphical model,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
7, | 1532-4435 | 55 |
PageRank | References | Authors |
5.69 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Bezerra de Andrade e Silva | 1 | 109 | 24.56 |
| Richard Scheines | 2 | 256 | 37.19 |
| Clark Glymour | 3 | 468 | 82.20 |
| Peter Spirtes | 4 | 616 | 101.07 |