Understand
Interventional causal models describe several joint distributions over some variables used to describe a system, one for each intervention setting.
- They provide a formal recipe for how to move between the different joint distributions and make predictions about the variables upon intervening on the system.
- Yet, it is difficult to formalise how we may change the underlying variables used to describe the system, say moving from fine-grained to coarse-grained variables.
- Here, we argue that compositionality is a desideratum for such model transformations and the associated errors: When abstracting a reference model M iteratively, first obtaining M' and then further simplifying that to obtain M'', we expect the composite transformation from M to M'' to exist and its error to be bounded by the errors incurred by each individual transformation step.