Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

Daniel Kumor, Carlos Cinelli, Elias Bareinboim
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5501-5510, 2020.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets %A Daniel Kumor %A Carlos Cinelli %A Elias Bareinboim %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-kumor20a %I PMLR %P 5501--5510 %U https://proceedings.mlr.press/v119/kumor20a.html %V 119 %X 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.
APA
Kumor, D., Cinelli, C. & Bareinboim, E.. (2020). Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:5501-5510 Available from https://proceedings.mlr.press/v119/kumor20a.html.

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