Inverse probability weighting (IPW) estimation has been popularly used to consistently estimate the average treatment effect (ATE). Its validity, however, is challenged by the presence of error-prone variables. In application, measurement error is ubiquitously present in data collection due to various reasons. Naively ignoring measurement error effects usually yields biased inference results. In this talk, I will discuss the IPW estimation with mismeasured outcome variables. The impact of measurement error for both continuous and discrete outcome variables will be examined. I will describe estimation procedures with the outcome misclassification effects accommodated. Consistency and efficiency will be investigated. Numerical studies will be reported to assess the performance of the proposed methods.