A Graphical Criterion for the Identification of Causal Effects in Linear Models
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. The paper establishes a sufficient criterion for the identifiability of all causal effects in such models as well as a procedure for estimating the causal effects from the observed covariance matrix.
Cite
Text
Brito and Pearl. "A Graphical Criterion for the Identification of Causal Effects in Linear Models." AAAI Conference on Artificial Intelligence, 2002. doi:10.5555/777092.777175Markdown
[Brito and Pearl. "A Graphical Criterion for the Identification of Causal Effects in Linear Models." AAAI Conference on Artificial Intelligence, 2002.](https://mlanthology.org/aaai/2002/brito2002aaai-graphical/) doi:10.5555/777092.777175BibTeX
@inproceedings{brito2002aaai-graphical,
title = {{A Graphical Criterion for the Identification of Causal Effects in Linear Models}},
author = {Brito, Carlos and Pearl, Judea},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2002},
pages = {533-538},
doi = {10.5555/777092.777175},
url = {https://mlanthology.org/aaai/2002/brito2002aaai-graphical/}
}