Graphs are powerful abstractions for modeling relationships and enabling data science tasks. In causal inference, Directed Acyclic Graphs (DAGs) serve as a key formalism, but they are typically handcrafted by experts and rarely treated as first-class data artifacts in graph data management systems. This paper presents a novel vision to align causal analysis with property graphs—the foundation of modern graph databases—by rethinking graph models to incorporate hypernodes, structural equations, and causality-aware query semantics. By unifying graph databases with causal reasoning, our approach enables the declarative expression of DAG manipulation operations along with interventions and counterfactuals, combining expressiveness with computational efficiency. We validate this vision through a proof-of-concept implementation supporting scalable causal queries over DAGs, ultimately aiming to make graph databases causally aware and support data-driven, personalized decision-making across several scientific domains.