Abstract

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.