For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.

在机器翻译任务中，为了实现两种语言之间的双向翻译，常常使用源语料库作为目标语料库，这涉及到使用相反方向的两个模型进行训练。然而，不同因素会影响适应速度，包括原始分布和修改后分布之间的因果依赖关系。本文通过考察简单的结构性因果模型中的敏感变量作用，对两个模型在四种场景的适应速度进行对比，并证明了所有干预情况下两个模型适应速度之间的联系。

公平感知因果模型的适应速度分析