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A novel (pre-)metric for causal graphs

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Causal discovery methods estimate a system’s underlying causal graph from observational data. But even if the true structure is known, it is not clear how to evaluate the performance of this estimator. In many cases, we use the causal graph to infer the system’s behaviour under specific interventions, i.e. manipulations of some of the variables. Different graphs may then result in different predicted behaviours. The structural intervention distance (SID) quantifies these differences and thereby defines a (pre-)metric between graphs. We briefly discuss recent results on identification of causal structure and show that they are closely related to the concept of SID . This talk does not require any prior knowledge about causal concepts.

This talk is part of the Statistics series.

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