Reasoning about Independence in Probabilistic Models of Relational Data
arXiv:1302.4381
Abstract
We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.
61 pages, substantial revisions to formalisms, theory, and related work
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Cited by in corpus (6)
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- Probabilistic Relational Model Benchmark Generation
- Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
- General Identification of Dynamic Treatment Regimes Under Interference
- Identification and Estimation of Causal Effects from Dependent Data