Representation of context-specific causal models with observational and interventional data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Duarte, E (Autor) FCUP; Solus, L (Autor) Outra

Revista

Título: Journal of the Royal Statistical Society. Series B: Statistical Methodology

ISSN: 1369-7412

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 0 Citações

Outras Informações

ID Authenticus: P-01A-8EW

DOI: 10.1093/jrsssb/qkaf059

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 44