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...
| Автори: | , , , |
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| Формат: | Conference item |
| Мова: | English |
| Опубліковано: |
Association for Uncertainty in Artificial Intelligence
2017
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