Feature-to-feature regression for a two-step conditional independence test
The algorithms for causal discovery and more broadly for learning the structure of graphical models require well calibrated and consistent conditional independence (CI) tests. We revisit the CI tests which are based on two-step procedures and involve regression with subsequent (unconditional) indepe...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Association for Uncertainty in Artificial Intelligence
2017
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