Learning from Pairwise Marginal Independencies
Abstract
We consider graphs that represent pairwise marginal independencies amongst a set of variables (for instance, the zero entries of a covariance matrix for normal data). We characterize the directed acyclic graphs (DAGs) that faithfully explain a given set of independencies, and derive algorithms to efficiently enumerate such structures. Our results map out the space of faithful causal models for a given set of pairwise marginal independence relations. This allows us to show the extent to which causal inference is possible without using conditional independence tests.
Cite
Text
Textor et al. "Learning from Pairwise Marginal Independencies." Conference on Uncertainty in Artificial Intelligence, 2015.Markdown
[Textor et al. "Learning from Pairwise Marginal Independencies." Conference on Uncertainty in Artificial Intelligence, 2015.](https://mlanthology.org/uai/2015/textor2015uai-learning/)BibTeX
@inproceedings{textor2015uai-learning,
title = {{Learning from Pairwise Marginal Independencies}},
author = {Textor, Johannes and Idelberger, Alexander and Liskiewicz, Maciej},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2015},
pages = {882-891},
url = {https://mlanthology.org/uai/2015/textor2015uai-learning/}
}