There is an increasing interest in algorithms to learn invariant correlations across training environments. A big share of the current proposals find theoretical support in the causality literature but, how useful are they in practice? The purpose of this note is to propose six linear low-dimensional problems—“unit tests”—to evaluate out-of-distribution generalization algorithms. Following initial experiments, none of three recently proposed alternatives pass these tests. By providing the code to automatically replicate all the results in this manuscript (https://github. com/anonymous), we hope that the development of unit tests becomes a standard stepping stone for researchers in out-of-distribution generalization.