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2019 UAI UAI 2019

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.

🚀 Conference Pioneer — UAI 2019
🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — relational causal discovery
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization

Authors

Sanghack Lee , Vasant Honavar

Topics

Artificial Intelligence > Core AI > Causal Inference Machine Learning > Learning Types > Unsupervised Learning

Keywords

causal discovery causal structure conditional independence conditional independence test relational datum relational causal discovery causal structure recovery
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