A Hybrid Causal Search Algorithm for Latent Variable Models
Abstract
Existing score-based causal model search algorithms such as \textit{GES} (and a speeded up version, \textit{FGS}) are asymptotically correct, fast, and reliable, but make the unrealistic assumption that the true causal graph does not contain any unmeasured confounders. There are several constraint-based causal search algorithms (e.g \textit{RFCI}, \emphFCI, or \emphFCI+) that are asymptotically correct without assuming that there are no unmeasured confounders, but often perform poorly on small samples. We describe a combined score and constraint-based algorithm, \emphGFCI, that we prove is asymptotically correct. On synthetic data, \textit{GFCI} is only slightly slower than \emphRFCI but more accurate than \textit{FCI}, \textit{RFCI} and \textit{FCI}+.
Cite
Text
Ogarrio et al. "A Hybrid Causal Search Algorithm for Latent Variable Models." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.Markdown
[Ogarrio et al. "A Hybrid Causal Search Algorithm for Latent Variable Models." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.](https://mlanthology.org/pgm/2016/ogarrio2016pgm-hybrid/)BibTeX
@inproceedings{ogarrio2016pgm-hybrid,
title = {{A Hybrid Causal Search Algorithm for Latent Variable Models}},
author = {Ogarrio, Juan Miguel and Spirtes, Peter and Ramsey, Joe},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
year = {2016},
pages = {368-379},
volume = {52},
url = {https://mlanthology.org/pgm/2016/ogarrio2016pgm-hybrid/}
}