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However, challenges can arise when accounting for treatment endogeneity, often intrinsic to these interventions. We study maximum likelihood estimation of stochastic frontier models when both the frontier and inefficiency depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism and explicitly model the density of the first and second\u2010stage error terms. 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