Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, and many other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond these subpopulations. In this paper, we introduce the ivmte package (Shea and Torgovitsky, 2019), which provides a flexible framework for implementing MTE methods in both point identified and partially identified settings.

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