Statistically Efficient Greedy Equivalence Search
Abstract
We establish the theoretical foundation for statistically efficient variants of the Greedy Equivalence Search algorithm. If each node in the generative structure has at most $k$ parents, we show that in the limit of large data, we can recover that structure using greedy search with operator scores that condition on at most $k$ variables. We present simple synthetic experiments that compare a backward-only variantof the new algorithm to GES using finite data, showing increasing benefit of the new algorithm as the complexity of the generative model increases.
Cite
Text
Chickering. "Statistically Efficient Greedy Equivalence Search." Uncertainty in Artificial Intelligence, 2020.Markdown
[Chickering. "Statistically Efficient Greedy Equivalence Search." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/chickering2020uai-statistically/)BibTeX
@inproceedings{chickering2020uai-statistically,
title = {{Statistically Efficient Greedy Equivalence Search}},
author = {Chickering, Max},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {241-249},
volume = {124},
url = {https://mlanthology.org/uai/2020/chickering2020uai-statistically/}
}