AI/ML

Towards Robust Relational Causal Discovery. (arXiv:1912.02390v1 [cs.LG])

We consider the problem of learning causal relationships from relational
data. Existing approaches rely on queries to a relational conditional
independence (RCI) oracle to establish and orient causal relations in such a
setting. In practice, queries to a RCI oracle have to be replaced by reliable
tests for RCI against available data. Relational data present several unique
challenges in testing for RCI. We study the conditions under which traditional
iid-based conditional independence (CI) tests yield reliable answers to RCI
queries against relational data. We show how to conduct CI tests against
relational data to robustly recover the underlying relational causal structure.
Results of our experiments demonstrate the effectiveness of our proposed
approach.

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