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This paper presents a novel vision to align causal analysis with property graphs\u2014the foundation of modern graph databases\u2014by 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.<\/jats:p>","DOI":"10.14778\/3749646.3749671","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T17:55:06Z","timestamp":1757008506000},"page":"4009-4016","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["What If: Causal Analysis with Graph Databases"],"prefix":"10.14778","volume":"18","author":[{"given":"Amedeo","family":"Pachera","sequence":"first","affiliation":[{"name":"Lyon1 University, CNRS Liris, Lyon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattia","family":"Palmiotto","sequence":"additional","affiliation":[{"name":"Lyon1 University, CNRS Liris, Lyon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angela","family":"Bonifati","sequence":"additional","affiliation":[{"name":"Lyon1 University, CNRS Liris &amp; IUF, Lyon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Mauri","sequence":"additional","affiliation":[{"name":"Lyon1 University, CNRS Liris, Lyon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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