Greedy equivalence search in the presence of latent confounders

Tom Claassen, Ioan G. Bucur
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:443-452, 2022.

Abstract

We investigate Greedy PAG Search (GPS) for score-based causal discovery over equivalence classes, similar to the famous Greedy Equivalence Search algorithm, except now in the presence of latent confounders. It is based on a novel characterization of Markov equivalence classes for MAGs, that not only improves state-of-the-art identification of Markov equivalence between MAGs to linear time complexity for sparse graphs, but also allows for efficient traversal over equivalence classes in the space of all MAGs. The resulting GPS algorithm is evaluated against several existing alternatives and found to show promising performance, both in terms of speed and accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-claassen22a, title = {Greedy equivalence search in the presence of latent confounders}, author = {Claassen, Tom and Bucur, Ioan G.}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {443--452}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/claassen22a/claassen22a.pdf}, url = {https://proceedings.mlr.press/v180/claassen22a.html}, abstract = {We investigate Greedy PAG Search (GPS) for score-based causal discovery over equivalence classes, similar to the famous Greedy Equivalence Search algorithm, except now in the presence of latent confounders. It is based on a novel characterization of Markov equivalence classes for MAGs, that not only improves state-of-the-art identification of Markov equivalence between MAGs to linear time complexity for sparse graphs, but also allows for efficient traversal over equivalence classes in the space of all MAGs. The resulting GPS algorithm is evaluated against several existing alternatives and found to show promising performance, both in terms of speed and accuracy.} }
Endnote
%0 Conference Paper %T Greedy equivalence search in the presence of latent confounders %A Tom Claassen %A Ioan G. Bucur %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-claassen22a %I PMLR %P 443--452 %U https://proceedings.mlr.press/v180/claassen22a.html %V 180 %X We investigate Greedy PAG Search (GPS) for score-based causal discovery over equivalence classes, similar to the famous Greedy Equivalence Search algorithm, except now in the presence of latent confounders. It is based on a novel characterization of Markov equivalence classes for MAGs, that not only improves state-of-the-art identification of Markov equivalence between MAGs to linear time complexity for sparse graphs, but also allows for efficient traversal over equivalence classes in the space of all MAGs. The resulting GPS algorithm is evaluated against several existing alternatives and found to show promising performance, both in terms of speed and accuracy.
APA
Claassen, T. & Bucur, I.G.. (2022). Greedy equivalence search in the presence of latent confounders. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:443-452 Available from https://proceedings.mlr.press/v180/claassen22a.html.

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