Dependency in DAG models with hidden variables

Conference item

Directed acyclic graph models with hidden variables have been much studied, particularly in view of their computational efficiency and connection with causal methods. In this paper we provide the circumstances under which it is possible for two variables to be identically equal, while all other observed variables stay jointly independent of them and mutually of each other. We find that this is possible if and only if the two variables are ‘densely connected’; in other words, if applications of identifiable causal interventions on the graph cannot (non-trivially) separate them. As a consequence of this, we can also allow such pairs of random variables have any bivariate joint distribution that we choose. This has implications for model search, since it suggests that we can reduce to only consider graphs in which densely connected vertices are always joined by an edge.

Authors: Evans, RJ

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Host title: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
• Pages: 813-822
• Series: Proceedings of Machine Learning Research
• Series number: 161
• Publication date: 2021-12-01
• Acceptance date: 2021-05-12
• Event title: 37th Conference on Uncertainty in Artificial Intelligence (UAI)
• Event location: Virtual event
• Event website: https://www.auai.org/uai2021/
• Event start date: 2021-07-27
• Event end date: 2021-07-30
• ISSN: 2640-3498
• Language: English
• Keywords: FFR