Detecting Causal Relations in the Presence of Unmeasured Variables
Artificial Intelligence
2013-03-26 v1
Abstract
The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it is possible to reliably infer the causal relation between two measured variables, regardless of whether latent variables are acting or not.
Keywords
Cite
@article{arxiv.1303.5754,
title = {Detecting Causal Relations in the Presence of Unmeasured Variables},
author = {Peter L. Spirtes},
journal= {arXiv preprint arXiv:1303.5754},
year = {2013}
}
Comments
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)