Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Abstract
We develop a polynomial-time algorithm for identification of structural coefficients in linear causal models that subsumes previous efficient state-of-the-art methods, unifying several disparate approaches to identification in this setting. Building on these results, we develop a procedure for identifying total causal effects in linear systems.
Cite
Text
Kumor et al. "Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets." International Conference on Machine Learning, 2020.Markdown
[Kumor et al. "Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/kumor2020icml-efficient/)BibTeX
@inproceedings{kumor2020icml-efficient,
title = {{Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets}},
author = {Kumor, Daniel and Cinelli, Carlos and Bareinboim, Elias},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {5501-5510},
volume = {119},
url = {https://mlanthology.org/icml/2020/kumor2020icml-efficient/}
}