Restricted Hidden Cardinality Constraints in Causal Models
This work addresses causal inference challenges for researchers in statistics and quantum physics, but it appears incremental as it builds on existing models with a specific promise.
The paper tackled the problem of identifying causal relations when unobserved variables have known cardinalities by deriving inequality constraints from d-separation, and explored applications to quantum systems.
Causal models with unobserved variables impose nontrivial constraints on the distributions over the observed variables. When a common cause of two variables is unobserved, it is impossible to uncover the causal relation between them without making additional assumptions about the model. In this work, we consider causal models with a promise that unobserved variables have known cardinalities. We derive inequality constraints implied by d-separation in such models. Moreover, we explore the possibility of leveraging this result to study causal influence in models that involve quantum systems.