Amortized Learning of Causal Topological Orderings and Fixed-Point Structural Causal Models

The authors propose a new framework to learn Structural Causal Models (SCMs) from observational data without instantiating any Directed Acyclic Graph (DAG). They introduce a fixed-point formulation of SCMs and show that the topological ordering (TO) of the causal variables is sufficient to uniquely recover the generating SCM in certain cases. They then design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, and then learns the generative fixed-point SCM on the ordered variables.