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Working with causal models at different levels of abstraction is an importantfeature of science. Existing work has already considered the problem ofexpressing formally the relation of abstraction between causal models. In thispaper, we focus on the problem of learning abstractions. We start by definingthe learning problem formally in terms of the optimization of a standardmeasure of consistency. We then point out the limitation of this approach, andwe suggest extending the objective function with a term accounting forinformation loss. We suggest a concrete measure of information loss, and weillustrate its contribution to learning new abstractions.

Towards Computing an Optimal Abstraction for Structural Causal Models