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Existing work in data fusion has covered identification of causal estimandswhen integrating data from heterogeneous sources. These results typicallyrequire additional assumptions to make valid estimation and inference. However,there is little literature on transporting and generalizing causal effects inmultiple-outcome setting, where the primary outcome is systematically missingon the study level but for which other outcome variables may serve as proxies.We review an identification result developed in ongoing work that utilizesinformation from these proxies to obtain more efficient estimators and thecorresponding key identification assumption. We then introduce methods forassessing the sensitivity of this approach to the identification assumption.

Sensitivity analysis for transportability in multi-study, multi-outcome settings