On the Identifiability of the Post-Nonlinear Causal Model

Understand

By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal model has demonstrated its excellent performance in distinguishing the cause from effect.

• However, its identifiability has not been properly addressed, and how to apply it in the case of more than two variables is also a problem.
• In this paper, we conduct a systematic investigation on its identifiability in the two-variable case.
• We show that this model is identifiable in most cases; by enumerating all possible situations in which the model is not identifiable, we provide sufficient conditions for its identifiability.