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) |
|---|---|
| State | Published - Jan 1 2019 |
| Event | 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 - Tel Aviv, Israel Duration: Jul 22 2019 → Jul 25 2019 |
Conference
| Conference | 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 |
|---|---|
| Country | Israel |
| City | Tel Aviv |
| Period | 7/22/19 → 7/25/19 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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Towards robust relational causal discovery. / Lee, Sanghack; Honavar, Vasant.
2019. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel.Research output: Contribution to conference › Paper
TY - CONF
T1 - Towards robust relational causal discovery
AU - Lee, Sanghack
AU - Honavar, Vasant
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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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M3 - Paper
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