The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.
翻译:本文件的目的是,在两个模型并非由相同变量组成的情况下,首次系统地探讨和界定等同因果模型,设想是,当两个模型商定所有“基本”因果信息时,只要它们能够用共同变量表示,两个模型就是等同的,我这样做的办法是着重探讨因果模型的两个主要特征,即结构关系和功能关系,特别是我界定了因果祖先的若干关系和因果充分性的若干关系,并要求这些关系中最一般的关系在等同模型之间保持。