Algebraic Equivalence Class Selection for Linear Structural Equation Models
Abstract
Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems. For causal discovery, two of these problems are especially important: the enumeration of the constraints imposed by a model, and deciding whether two graphs define the same statistical model. We show how the half-trek criterion can be used to make progress in both of these problems. We apply our theoretical results to a small-scale model selection problem, and find that taking the additional algebraic constraints into account may lead to significant improvements in model selection accuracy.
Cite
Text
van Ommen and Mooij. "Algebraic Equivalence Class Selection for Linear Structural Equation Models." Conference on Uncertainty in Artificial Intelligence, 2017.Markdown
[van Ommen and Mooij. "Algebraic Equivalence Class Selection for Linear Structural Equation Models." Conference on Uncertainty in Artificial Intelligence, 2017.](https://mlanthology.org/uai/2017/vanommen2017uai-algebraic/)BibTeX
@inproceedings{vanommen2017uai-algebraic,
title = {{Algebraic Equivalence Class Selection for Linear Structural Equation Models}},
author = {van Ommen, Thijs and Mooij, Joris M.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2017},
url = {https://mlanthology.org/uai/2017/vanommen2017uai-algebraic/}
}