Estimating a Causal Order among Groups of Variables in Linear Models
Abstract
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among scalar random variables. We generalize existing methods to apply to collections of multi-dimensional random vectors, focusing on techniques applicable to linear models. The performance of the resulting algorithms is evaluated and compared in simulations, which show that our methods can, in many cases, provide useful information on causal relationships even for relatively small sample sizes.
Cite
@article{arxiv.1207.1977,
title = {Estimating a Causal Order among Groups of Variables in Linear Models},
author = {Doris Entner and Patrik O. Hoyer},
journal= {arXiv preprint arXiv:1207.1977},
year = {2012}
}
Comments
To appear at the International Conference on Artificial Neural Networks 2012 (proceedings to be published in LNCS, Springer); To be presented at the UAI Workshop on Causal Structure Learning 2012