报告题目：Causal Inference with Measurement Error in Outcomes
报告人：Grace Y. Yi
报告人单位：University of Waterloo
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.