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.

利用结构因果模型（SCM）的发展，建立了图形模型来探索机器学习技术背后的因果机制。随着机器学习应用的复杂性提高，单世界干预因果分析遇到了理论自适应的限制。因此，跨世界反事实方法扩展了我们对因果性的理解，使我们能够对替代场景进行假设推理。然而，涉及跨世界变量的联合参与，包括反事实变量和真实世界变量，对图形模型的构建提出了挑战。双网络是一个微妙的尝试，建立了一种互惠关系，以弥合图形建模与反事实引入之间的差距，尽管还有改进的空间。在这方面，我们展示了双网络在某些跨世界反事实场景中的理论失效。为此，我们提出了一种新颖的传送门理论，以建立反事实的通用且简单的图形表示，为连接多个世界的传送门变量提供了准则。在理论应用中，我们确定引入所提出的传送门理论可以直接从跨世界 SCM 中获得反事实变量和真实世界变量之间的条件独立性，而不需要复杂的代数推导。因此，我们可以通过跨世界符号推导进一步确定反事实因果效应。我们展示了传送门理论在实际应用中的普遍性。坚持遵循所提出的理论，我们构建了一个即插即用的模块，并通过基准实验证实了其有效性。

传送者理论：一种建模跨界反事实因果关系的通用且简单方法

Teleporter Theory: A General and Simple Approach for Modeling  Cross-World Counterfactual Causality

One-shot learning has become an important research topic in the last decade
with many real-world applications. The goal of one-shot learning is to classify
unlabeled instances when there is only one labeled example per class.
Conventional problem setting of one-shot learning mainly focuses on the data
that is already in feature space (such as images). However, the data instances
in real-world applications are often more complex and feature vectors may not
be available. In this paper, we study the problem of one-shot learning on
attributed sequences, where each instance is composed of a set of attributes
(e.g., user profile) and a sequence of categorical items (e.g., clickstream).
This problem is important for a variety of real-world applications ranging from
fraud prevention to network intrusion detection. This problem is more
challenging than conventional one-shot learning since there are dependencies
between attributes and sequences. We design a deep learning framework OLAS to
tackle this problem. The proposed OLAS utilizes a twin network to generalize
the features from pairwise attributed sequence examples. Empirical results on
real-world datasets demonstrate the proposed OLAS can outperform the
state-of-the-art methods under a rich variety of parameter settings.

本文介绍了如何使用 deep learning 模型 OLAS 来解决 One-shot learning 在 attributed sequences 上的问题，并通过在真实数据集上进行实验验证了其优越性。