Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data

Learning invariant causal structure often relies on conditional independence testing and assumption of independent and identically distributed data. Recent work has explored inferring invariant causal structure using data coming from different environments. These approaches are based on independent causal mechanism (ICM) principle which postulates that the cause mechanism is independent of the effect given cause mechanism. Despite its wide application in machine learning and causal inference, there lacks a statistical formalization of what independent mechanism means. Here we present Causal de Finetti which offers a first statistical formalization of ICM principle.