Name
Abstract | ||
|---|---|---|
Causal knowledge discovery is an essential task in many disciplines. Inferring the knowledge of causal directions from the measurement data of two correlated variables is one of the most basic but non-trivial problems in the research of causal discovery. Most of the existing methods assume that at least one of the variables is strictly measured. In practice, uncertain data with observation error is widely exists and is unavoidable for both the cause and the effect. Correct causal relationships will be blurred by such noise. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.engappai.2017.05.007 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Causal direction,Errors-in-variables model,EM algorithm | Data mining,Data set,Inference,Expectation–maximization algorithm,Computer science,Regression analysis,Principal stratification,Uncertain data,Knowledge extraction,Artificial intelligence,Causal system,Machine learning | Journal |
Volume | ISSN | Citations |
65 | 0952-1976 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulai Zhang | 1 | 5 | 2.54 |
| Weifeng Ma | 2 | 61 | 7.46 |
| Guiming Luo | 3 | 69 | 28.79 |