{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:25:40Z","timestamp":1759623940659,"version":"build-2065373602"},"reference-count":46,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft DFG","award":["314838170"],"award-info":[{"award-number":["314838170"]}]},{"DOI":"10.13039\/100006129","name":"FCT","doi-asserted-by":"publisher","award":["2020.01933.CEECIND"],"award-info":[{"award-number":["2020.01933.CEECIND"]}],"id":[{"id":"10.13039\/100006129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006753","name":"CMUP","doi-asserted-by":"publisher","award":["UIDB\/00144\/2020"],"award-info":[{"award-number":["UIDB\/00144\/2020"]}],"id":[{"id":"10.13039\/501100006753","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"name":"G\u00f6ran Gustafsson Prize for Young Researchers"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We address the problem of representing context-specific causal models based on both observational and experimental data collected under general (e.g. hard or soft) interventions by introducing a new family of context-specific conditional independence models called CStrees. This family is defined via a novel factorization criterion that allows for a generalization of the factorization property defining general interventional directed acyclic graph (DAG) models. We derive a graphical characterization of model equivalence for observational CStrees that extends the Verma and Pearl criterion for DAGs. This characterization is then extended to CStree models under general, context-specific interventions. To obtain these results, we formalize a notion of context-specific intervention that can be incorporated into concise graphical representations of CStree models. We relate CStrees to other context-specific models, showing that the families of DAGs, CStrees, labelled DAGs, and staged trees form a strict chain of inclusions. We then present an algorithm for learning CStrees from a combination of observational and interventional data where the intervention targets are assumed to be unknown with hard or soft and possibly context-specific effects. The algorithm, evaluated on simulated and real data, performs well in the recovery of context-specific dependence structure as well as context-specific interventional perturbations.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkaf059","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T04:00:14Z","timestamp":1759550414000},"source":"Crossref","is-referenced-by-count":0,"title":["Representation of context-specific causal models with observational and interventional data"],"prefix":"10.1093","author":[{"given":"Eliana","family":"Duarte","sequence":"first","affiliation":[{"name":"Departmento de Matem\u00e1tica, Faculdade de Ci\u00eancias, Universidade do Porto , Porto ,","place":["Portugal"]}]},{"given":"Liam","family":"Solus","sequence":"additional","affiliation":[{"name":"Department of Mathematics, KTH , Stockholm 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