Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking 'what qualifies an action for being an intervention on the variable X' raises the question whether the action impacted all other variables only through X or directly, which implicitly refers to a causal model. To avoid this known circularity, we instead suggest a notion of 'phenomenological causality' whose basic concept is a set of elementary actions. Then the causal structure is defined such that elementary actions change only the causal mechanism at one node (e.g. one of the causal conditionals in the Markov factorization). This way, the Principle of Independent Mechanisms becomes the defining property of causal structure in domains where causality is a more abstract phenomenon rather than being an objective fact relying on hard-wired causal links between tangible objects. We describe this phenomenological approach to causality for toy and hypothetical real-world examples and argue that it is consistent with the causal Markov condition when the system under consideration interacts with other variables that control the elementary actions.
翻译:有关现实生活中因果关系的讨论往往会考虑到一些变量,这些变量的因果关系定义不明确,因为对各变量的干预概念是模糊的。问“什么有资格作为对变数X的干预”引起这样的问题,即行动是否仅仅通过X或直接影响到所有其他变量,这暗示了因果关系模式。为了避免这种已知的循环性,我们建议一种“血清因果关系”的概念,其基本概念是一套基本行动。然后,因果结构的定义是,基本行动只改变一个节点的因果机制(例如,马尔科夫因子化的因果机制之一)。这样,独立机制原则就成了在因果关系是一个比较抽象的现象而不是客观事实的领域界定因果结构的属性,这些领域的因果结构依赖于有形物体之间的硬线性因果联系。我们用这种因果性方法来描述毒性和假设现实世界实例的因果关系,并说,当所考虑的系统与控制基本行动的其他变数相互作用时,它与因果性马尔科夫的条件是一致的。