Representation of Context-Specific Causal Models with Observational and Interventional Data
This work addresses the problem of representing context-specific causal models for researchers in causal inference, offering a novel graphical framework that extends existing methods.
The paper introduces CStrees, a new family of context-specific conditional independence models that generalize interventional DAG models to handle both observational and experimental data under general interventions, and applies them to a real dataset to reveal context-specific dependence structures.
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 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, labeled DAGs and staged trees form a strict chain of inclusions. We end with an application of interventional CStree models to a real data set, revealing the context-specific nature of the data dependence structure and the soft, interventional perturbations.