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...

書誌詳細
主要な著者:	Zhang, Q, Filippi, S, Flaxman, S, Sejdinovic, D
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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