Speaker
Beata Zjawin
(Gdansk University)
Description
When some variables in a directed acyclic graph (DAG) are hidden, a notoriously complicated set of constraints on the distribution of observed variables is implied. In this talk, we present inequality constraints implied by graphical criteria in hidden variable DAGs. The constraints can intuitively be understood to follow from the fact that the capacity of variables along a causal pathway to convey information is restricted by their entropy. For DAGs that exhibit e-separation relations, we present entropic inequality constraints and we show how they can be used to learn about the true causal model from an observed data distribution (arXiv:2107.07087).
External references
- 23040109
- 3aa19733-1ee0-413a-aa7a-0435740aa000
- 78446978-e58b-43e4-a6cd-6f3f075b4f79
