ICML 2020poster21 citations
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Daniel Kumor, Carlos Cinelli, Elias Bareinboim
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.
BibTeX
@InProceedings{pmlr-v119-kumor20a,
title = {Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets},
author = {Kumor, Daniel and Cinelli, Carlos and Bareinboim, Elias},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {5501--5510},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/kumor20a/kumor20a.pdf},
url = {https://proceedings.mlr.press/v119/kumor20a.html},
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.}
}