A Sound and Complete Algorithm for Learning Causal Models from Relational Data release_j65c6midgnfjpdtgudc3cox7wa

by Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen

Released as a report .

2013  

Abstract

The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks. Recent developments in the theory and representation of relational models support lifted reasoning about conditional independence. This enables a powerful constraint for orienting bivariate dependencies and forms the basis of a new algorithm for learning structure. We present the relational causal discovery (RCD) algorithm that learns causal relational models. We prove that RCD is sound and complete, and we present empirical results that demonstrate effectiveness.
In text/plain format

Archived Files and Locations

application/pdf  1.1 MB
file_yvi4ywurkbbmpjqehjw32wk2ty
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  report
Stage   submitted
Date   2013-09-26
Version   v1
Language   en ?
Number  UAI-P-2013-PG-371-380
arXiv  1309.6843v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 85fde758-a9c4-4580-a6b9-688b43d894b3
API URL: JSON