On Learning Causal Models from Relational Data

Authors

  • Sanghack Lee Pennsylvania State University
  • Vasant Honavar Pennsylvania State University

DOI:

https://doi.org/10.1609/aaai.v30i1.10417

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.

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Published

2016-03-05

How to Cite

Lee, S., & Honavar, V. (2016). On Learning Causal Models from Relational Data. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10417

Issue

Section

Technical Papers: Reasoning under Uncertainty