Feature-to-feature regression for a two-step conditional independence test

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...

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Bibliografische gegevens
Hoofdauteurs:	Zhang, Q, Filippi, S, Flaxman, S, Sejdinovic, D
Formaat:	Conference item
Taal:	English
Gepubliceerd in:	Association for Uncertainty in Artificial Intelligence 2017

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