  This paper studies identification of the marginal treatment effect (MTE) when
a binary treatment variable is misclassified. We show under standard
assumptions that the MTE is identified as the derivative of the conditional
expectation of the observed outcome given the true propensity score, which is
partially identified. We characterize the identified set for this propensity
score, and then for the MTE. We show under some mild regularity conditions that
the sign of the MTE is locally identified. We use our MTE bounds to derive
bounds on other commonly used parameters in the literature. We show that our
bounds are tighter than the existing bounds for the local average treatment
effect. We illustrate the practical relevance of our derived bounds through
some numerical and empirical results.
