Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
Machine Learning
2017-12-05 v4
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 ap- proach 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.
Cite
@article{arxiv.1508.01717,
title = {Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams},
author = {Christopher Nowzohour and Marloes H. Maathuis and Robin J. Evans and Peter Bühlmann},
journal= {arXiv preprint arXiv:1508.01717},
year = {2017}
}