Estimation of Average Treatment Effects With Misclassification

This paper provides conditions for identification, and an associated estimator, of the average effect of a binary treatment or policy on a scalar outcome in models where treatment may be misclassified. Misclassification probabilities and the true probability of treatment are also identified. Misclassification occurs when treatment is measured with error, that is, some units are reported to have received treatment when they actually have not, and vice versa. Conditional outcomes, treatment probabilities, and misclassification probabilities are all nonparametric. The identifying assumption is an exclusion restriction, specifically, the existence of a variable that can take on at least three different values, affects the decision to treat, and is conditionally independent of the conditional misclassification probabilities and the average treatment effect. JEL Codes: C14, C13.