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Machine learning can benefit from causal discovery for interpretation andfrom causal inference for generalization. In this line of research, a fewinvariant learning algorithms for out-of-distribution (OOD) generalization havebeen proposed by using multiple training environments to find invariantrelationships. Some of them are focused on causal discovery as Invariant CausalPrediction (ICP), which finds causal parents of a variable of interest, andsome directly provide a causal optimal predictor that generalizes well in OODenvironments as Invariant Risk Minimization (IRM). This group of algorithmsworks under the assumption of multiple environments that represent differentinterventions in the causal inference context. Those environments are notnormally available when working with observational data and real-worldapplications. Here we propose a method to generate them in an efficient way. Weassess the performance of this unsupervised learning problem by implementingICP on simulated data. We also show how to apply ICP efficiently integratedwith our method for causal discovery. Finally, we proposed an improved versionof our method in combination with ICP for datasets with multiple covariateswhere ICP and other causal discovery methods normally degrade in performance.

A step towards the applicability of algorithms based on invariant causal learning on observational data