Surrogate Outcomes and Transportability

Identification of causal effects is one of the most fundamental tasks of causal inference. We consider a variant of the identifiability problem where a causal effect of interest is not identifiable from observational data alone but some experimental data is available for the identification task. This corresponds to a real-world setting where experiments were conducted on a set of variables, which we call surrogate outcomes, but the variables of interest were not measured. This problem is a generalization of identifiability using surrogate experiments and we label it as surrogate outcome identifiability and show that the concept of transportability provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.