Nested markov properties for acyclic directed mixed graphs - Richardson, T; Robins, JM; Evans, RJ; Shpitser, I (Institute of Mathematical Statistics, 2023)
Conditional independence models associated with directed acyclic graphs (DAGs) may be characterized in at least three different ways: via a factorization, the global Markov property (given by the dseparation criterion), and the local Markov property. Marginals of DAG models also imply equality constraints that are not conditional independences; the well-known ‘Verma constraint’ is an example. Constraints of this type are used for testing edges, and in a computationally efficient marginalization scheme via variable elimination. We show that equality constraints like the ‘Verma constraint’ can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via ordered local and global Markov properties, and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We prove that marginal distributions of DAG models lie in this model, and that a set of these constraints given by Tian provides an alternative definition of the model. Finally, we show that the fixing operation used to define the model leads to a particularly simple characterization of identifiable causal effects in hidden variable causal DAG models.