Electronic Journal of Statistics

Distributional equivalence and structure learning for bow-free acyclic path diagrams

Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, and Peter Bühlmann

Full-text: Open access

Abstract

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.

Article information

Source
Electron. J. Statist. Volume 11, Number 2 (2017), 5342-5374.

Dates
Received: October 2016
First available in Project Euclid: 28 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1514430421

Digital Object Identifier
doi:10.1214/17-EJS1372

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

Nowzohour, Christopher; Maathuis, Marloes H.; Evans, Robin J.; Bühlmann, Peter. Distributional equivalence and structure learning for bow-free acyclic path diagrams. Electron. J. Statist. 11 (2017), no. 2, 5342--5374. doi:10.1214/17-EJS1372. https://projecteuclid.org/euclid.ejs/1514430421


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