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2017 Distributional equivalence and structure learning for bow-free acyclic path diagrams
Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, Peter Bühlmann
Electron. J. Statist. 11(2): 5342-5374 (2017). DOI: 10.1214/17-EJS1372


We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain hidden variables. We present a first method for this problem using a greedy score-based search algorithm. We also prove some necessary and some sufficient conditions for distributional equivalence of BAPs which are used in an algorithmic approach to compute (nearly) equivalent model structures. This allows us to infer lower bounds of causal effects. We also present applications to real and simulated datasets using our publicly available R-package.


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Christopher Nowzohour. Marloes H. Maathuis. Robin J. Evans. Peter Bühlmann. "Distributional equivalence and structure learning for bow-free acyclic path diagrams." Electron. J. Statist. 11 (2) 5342 - 5374, 2017.


Received: 1 October 2016; Published: 2017
First available in Project Euclid: 28 December 2017

zbMATH: 1384.62209
MathSciNet: MR3743733
Digital Object Identifier: 10.1214/17-EJS1372

Keywords: Causal inference , distributional equivalence , greedy search , hidden variables , latent variables , path diagrams , structural equation models , structure learning


Vol.11 • No. 2 • 2017
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