Generalized Instrumental Variables
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.
Cite
Text
Brito and Pearl. "Generalized Instrumental Variables." Conference on Uncertainty in Artificial Intelligence, 2002.Markdown
[Brito and Pearl. "Generalized Instrumental Variables." Conference on Uncertainty in Artificial Intelligence, 2002.](https://mlanthology.org/uai/2002/brito2002uai-generalized/)BibTeX
@inproceedings{brito2002uai-generalized,
title = {{Generalized Instrumental Variables}},
author = {Brito, Carlos and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2002},
pages = {85-93},
url = {https://mlanthology.org/uai/2002/brito2002uai-generalized/}
}