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Many treatment variables used in empirical applications nest multipleunobserved versions of a treatment. I show that instrumental variable (IV)estimands for the effect of a composite treatment are IV-specific weightedaverages of effects of unobserved component treatments. Differences between IVsin unobserved component compliance produce differences in IV estimands evenwithout treatment effect heterogeneity. I describe a monotonicity conditionunder which IV estimands are positively-weighted averages of unobservedcomponent treatment effects. Next, I develop a method that allows instrumentsthat violate this condition to contribute to estimation of treatment effects byallowing them to place nonconvex, outcome-invariant weights on unobservedcomponent treatments across multiple outcomes. Finally, I apply the method toestimate returns to college, finding wage returns that range from 7\% to 30\%over the life cycle. My findings emphasize the importance of leveraginginstrumental variables that do not shift individuals between versions oftreatment, as well as the importance of policies that encourage students toattend "high-return college" in addition to those that encourage "high-returnstudents" to attend college.

Interpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education