| ID:
596814.0,
MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung) / Abt. Schölkopf (Empirical Inference) |
| Kernel-based Conditional Independence Test and Application in Causal Discovery |
| Authors: | Zhang, K.; Peters, J.; Janzing, D.; Schölkopf, B. |
| Publisher: | AUAI Press |
| Place of Publication: | Corvallis, OR |
| Date of Publication (YYYY-MM-DD): | 2011-07-01 |
| Title of Proceedings: | 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) |
| Start Page: | 804 |
| End Page: | 813 |
| Physical Description: | 9 |
| Place of Conference/Meeting: | Barcelona, Spain |
| Review Status: | not specified |
| Audience: | Not Specified |
| Intended Educational Use: | No |
| Abstract / Description: | Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery. Due to the curse of dimensionality, testing for conditional independence of continuous variables is particularly challenging. We propose a Kernel-based Conditional Independence test (KCI-test), by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional
independence. The proposed method is computationally efficient and easy to implement. Experimental results show that it outperforms other methods, especially when the conditioning set is large or the sample size is not very large, in which case other methods encounter difficulties. |
| External Publication Status: | published |
| Document Type: | Conference-Paper |
| Communicated by: | Heide Klooz |
| Affiliations: | MPI für Intelligente Systeme/Abt. Schölkopf
|
| Identifiers: | URL:http://www.kyb.tuebingen.mpg.de/fileadmin/user_upl...
LOCALID:ZhangPJS2011 |
|
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