Towards Robust Relational Causal Discovery

Sanghack Lee, Vasant Honavar
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:345-355, 2020.

Abstract

We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based CI tests yield reliable answers to RCI queries against relational data. We show how to conduct CI tests against relational data to robustly recover the underlying relational causal structure. Results of our experiments demonstrate the effectiveness of our proposed approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v115-lee20a, title = {Towards Robust Relational Causal Discovery}, author = {Lee, Sanghack and Honavar, Vasant}, booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, pages = {345--355}, year = {2020}, editor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, series = {Proceedings of Machine Learning Research}, month = {22--25 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v115/lee20a/lee20a.pdf}, url = {https://proceedings.mlr.press/v115/lee20a.html}, abstract = {We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based CI tests yield reliable answers to RCI queries against relational data. We show how to conduct CI tests against relational data to robustly recover the underlying relational causal structure. Results of our experiments demonstrate the effectiveness of our proposed approach.} }
Endnote
%0 Conference Paper %T Towards Robust Relational Causal Discovery %A Sanghack Lee %A Vasant Honavar %B Proceedings of The 35th Uncertainty in Artificial Intelligence Conference %C Proceedings of Machine Learning Research %D 2020 %E Ryan P. Adams %E Vibhav Gogate %F pmlr-v115-lee20a %I PMLR %P 345--355 %U https://proceedings.mlr.press/v115/lee20a.html %V 115 %X We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based CI tests yield reliable answers to RCI queries against relational data. We show how to conduct CI tests against relational data to robustly recover the underlying relational causal structure. Results of our experiments demonstrate the effectiveness of our proposed approach.
APA
Lee, S. & Honavar, V.. (2020). Towards Robust Relational Causal Discovery. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:345-355 Available from https://proceedings.mlr.press/v115/lee20a.html.

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