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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 345-355 |
| Number of pages | 11 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 115 |
| State | Published - 2019 |
| Event | 35th Uncertainty in Artificial Intelligence Conference, UAI 2019 - Tel Aviv, Israel Duration: Jul 22 2019 → Jul 25 2019 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability