Generalized Instrumental Variables
Artificial Intelligence
2013-01-07 v1
Abstract
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which allows its application to models with few conditional independeces.
Keywords
Cite
@article{arxiv.1301.0560,
title = {Generalized Instrumental Variables},
author = {Carlos Brito and Judea Pearl},
journal= {arXiv preprint arXiv:1301.0560},
year = {2013}
}
Comments
Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)