Towards Robust Relational Causal Discovery

UAI 2019 pp. 345-355

/uai/2019/lee2019uai-robust/

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.

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Cite

Text

Lee and Honavar. "Towards Robust Relational Causal Discovery." Uncertainty in Artificial Intelligence, 2019.

Markdown

[Lee and Honavar. "Towards Robust Relational Causal Discovery." Uncertainty in Artificial Intelligence, 2019.](https://mlanthology.org/uai/2019/lee2019uai-robust/)

BibTeX

@inproceedings{lee2019uai-robust,
  title     = {{Towards Robust Relational Causal Discovery}},
  author    = {Lee, Sanghack and Honavar, Vasant},
  booktitle = {Uncertainty in Artificial Intelligence},
  year      = {2019},
  pages     = {345-355},
  volume    = {115},
  url       = {https://mlanthology.org/uai/2019/lee2019uai-robust/}
}