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On Learning Causal Models from Relational Data

Cited 11 time in Web of Science Cited 25 time in Scopus
Authors

Lee, Sanghack; Honavar, Vasant

Issue Date
2016
Publisher
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
Citation
THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.3263-3270
Abstract
Many applications call for learning causal models from relational data. We investigate Relational Causal Models (RCM) under relational counterparts of adjacency-faithfulness and orientation-faithfulness, yielding a simple approach to identifying a subset of relational d-separation queries needed for determining the structure of an RCM using d-separation against an unrolled DAG representation of the RCM. We provide original theoretical analysis that offers the basis of a sound and efficient algorithm for learning the structure of an RCM from relational data. We describe RCD-Light, a sound and efficient constraint-based algorithm that is guaranteed to yield a correct partially-directed RCM structure with at least as many edges oriented as in that produced by RCD, the only other existing algorithm for learning RCM. We show that unlike RCD, which requires exponential time and space, RCD-Light requires only polynomial time and space to orient the dependencies of a sparse RCM.
ISSN
2159-5399
URI
https://hdl.handle.net/10371/201566
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  • Graduate School of Data Science
Research Area Causal Decision Making, Causal Discovery, Causal Inference

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