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 languageEnglish (US)
StatePublished - Jan 1 2019
Event35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 - Tel Aviv, Israel
Duration: Jul 22 2019Jul 25 2019

Conference

Conference35th Conference on Uncertainty in Artificial Intelligence, UAI 2019
CountryIsrael
CityTel Aviv
Period7/22/197/25/19

Fingerprint

Testing
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Lee, S., & Honavar, V. (2019). Towards robust relational causal discovery. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel.
Lee, Sanghack ; Honavar, Vasant. / Towards robust relational causal discovery. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel.
@conference{94ce10268995405ca990542f71db63c6,
title = "Towards robust relational causal discovery",
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.",
author = "Sanghack Lee and Vasant Honavar",
year = "2019",
month = "1",
day = "1",
language = "English (US)",
note = "35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 ; Conference date: 22-07-2019 Through 25-07-2019",

}

Lee, S & Honavar, V 2019, 'Towards robust relational causal discovery' Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, 7/22/19 - 7/25/19, .

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 conferencePaper

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.

UR - http://www.scopus.com/inward/record.url?scp=85073207532&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073207532&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85073207532

ER -

Lee S, Honavar V. Towards robust relational causal discovery. 2019. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel.