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We address the problem of estimating the effect of intervening on a set ofvariables X from experiments on a different set, Z, that is more accessible tomanipulation. This problem, which we call z-identifiability, reduces toordinary identifiability when Z = empty and, like the latter, can be givensyntactic characterization using the do-calculus [Pearl, 1995; 2000]. Weprovide a graphical necessary and sufficient condition for z-identifiabilityfor arbitrary sets X,Z, and Y (the outcomes). We further develop a completealgorithm for computing the causal effect of X on Y using information providedby experiments on Z. Finally, we use our results to prove completeness ofdo-calculus relative to z-identifiability, a result that does not follow fromcompleteness relative to ordinary identifiability.

Causal Inference by Surrogate Experiments: z-Identifiability