Randomization Tests for Relational Learning

Algorithms for relational learning and proposi-tional learning face different statistical challenges. In contrast to propositional learners, rela-tional learners often make statistical inferences about data that exhibit linkage and autocorrela-tion. Recent work has shown that these characteristics of relational data can bias inferences made by relational learners. In this paper, we develop a novel variant of a known statistical procedure — a randomization test — that produces accurate hypothesis tests for relational data. We show that our procedure produces un-biased inferences in situations where more obvious adaptations of existing randomization tests fail.