上下文特定因果模型的表示：基于观测数据与干预数据
Representation of Context-Specific Causal Models with Observational and Interventional Data
AI总结：
本文提出CStrees模型，通过新因子分解准则表示上下文特定因果模型，扩展DAG等价性刻画至干预场景，并应用于真实数据揭示依赖结构。
摘要：
我们针对基于在一般（例如硬或软）干预下收集的观测和实验数据来表示上下文特定因果模型的问题，引入了一类新的上下文特定条件独立模型，称为CStrees。该族通过一种新颖的因子分解准则定义，该准则允许对定义一般干预DAG模型的因子分解性质进行推广。我们推导了观测CStrees模型等价性的图形化刻画，该刻画扩展了DAG的Verma和Pearl准则。这一刻画随后被扩展到一般、上下文特定干预下的CStree模型。为获得这些结果，我们形式化了上下文特定干预的概念，该概念可被纳入CStree模型的简洁图形表示中。我们将CStrees与其他上下文特定模型联系起来，表明DAG、CStrees、标记DAG和分段树这些族构成一个严格的包含链。最后，我们将干预CStree模型应用于一个真实数据集，揭示了数据依赖结构的上下文特定性质以及软干预扰动。
英文摘要：
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.
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