Leveraging the development of structural causal model (SCM), researchers can establish graphical models for exploring the causal mechanisms behind machine learning techniques. As the complexity of machine learning applications rises, single-world interventionism causal analysis encounters theoretical adaptation limitations. Accordingly, cross-world counterfactual approach extends our understanding of causality beyond observed data, enabling hypothetical reasoning about alternative scenarios. However, the joint involvement of cross-world variables, encompassing counterfactual variables and real-world variables, challenges the construction of the graphical model. Twin network is a subtle attempt, establishing a symbiotic relationship, to bridge the gap between graphical modeling and the introduction of counterfactuals albeit with room for improvement in generalization. In this regard, we demonstrate the theoretical breakdowns of twin networks in certain cross-world counterfactual scenarios. To this end, we propose a novel teleporter theory to establish a general and simple graphical representation of counterfactuals, which provides criteria for determining teleporter variables to connect multiple worlds. In theoretical application, we determine that introducing the proposed teleporter theory can directly obtain the conditional independence between counterfactual variables and real-world variables from the cross-world SCM without requiring complex algebraic derivations. Accordingly, we can further identify counterfactual causal effects through cross-world symbolic derivation. We demonstrate the generality of the teleporter theory to the practical application. Adhering to the proposed theory, we build a plug-and-play module, and the effectiveness of which are substantiated by experiments on benchmarks.

翻译：借助结构因果模型（SCM）的发展，研究者能够建立图模型以探索机器学习技术背后的因果机制。随着机器学习应用复杂度的提升，单世界干预主义因果分析面临理论适应性的局限。相应地，跨世界反事实方法将我们对因果关系的理解扩展到观测数据之外，使得对替代场景的假设性推理成为可能。然而，跨世界变量（包括反事实变量与现实世界变量）的联合参与对图模型的构建提出了挑战。孪生网络是一种建立共生关系的巧妙尝试，旨在弥合图建模与反事实引入之间的鸿沟，但其泛化能力仍有提升空间。对此，我们论证了孪生网络在特定跨世界反事实场景中的理论局限。为此，我们提出了一种新颖的传送门理论，以建立一种通用且简单的反事实图形表示方法，该方法为确定连接多个世界的传送门变量提供了准则。在理论应用方面，我们证实引入所提出的传送门理论可直接从跨世界SCM中获得反事实变量与现实世界变量间的条件独立性，无需复杂的代数推导。据此，我们能够进一步通过跨世界符号推导识别反事实因果效应。我们通过实际应用验证了传送门理论的普适性。遵循所提理论，我们构建了一个即插即用模块，其在基准测试上的实验效果验证了该模块的有效性。